Efficient Control Method Research Articles

Phytophthora heterospora is the causal agent of black rot disease on the orchid Cattleya leopoldii in Brazil.

Widely produced and marketed worldwide, orchids suffers from several diseases that have a negative effect on production. Black rot disease is among the most common and severe disease, characterized by black spots of rot on leaves, pseudobulbs and roots, which usually lead to the plant death. The world literature lists some Phytophthora species as causal agents of the disease, but there is no advanced study on the etiology of this disease in Brazil, which makes it difficult to determine an efficient control method. This work aims to contribute to the study of the etiology of black rot on Cattleya leopoldii in Brazil. Severely diseased C. leopoldii plants with typical symptoms of black rot, collected from a commercial orchid farm in Brazil, were taken to the laboratory and used to isolate the pathogen. Based on morphological characters analysis combined with molecular data, the isolates were identified as belonging to the species Phytophthora heterospora. This is the first worldwide report of P. heterospora causing black rot disease on orchids.

Efficacy of Pre and Post Emergence Herbicides against Complex Weed Flora, Yield and Economics of Blackgram under High Hills Wet Temperate Zone of Himachal Pradesh

Background: Implementing integrated strategies for weed management is essential in maximizing blackgram yield. Unchecked weed proliferation leads to substantial decrease in crop productivity. Methods: The current field experiment was conducted at Salooni, Himachal Pradesh, to evaluate the efficacy of different integrated weed management practices during Kharif 2022 and 2023. Eleven treatments targeting weed control were evaluated both at pre and post - emergence stages. Result: The predominant weeds were Echinochloa colona, Galinsoga parviflora, Cyperus species and other broad leaf weeds were Plantago lanceolata, Trifolium repens, Trianthema species, Ageratum conyzoides. Data on weed dynamics revealed that; after weed-free treatment, the application of Imazethapyr 75 g/ha as PE fb HW at 30 DAS; Pendimethalin 1.0 kg/ha as PE fb HW at 30 DAS and Pendimethalin 1.0 kg/ha + imazethapyr 75 g/ha (PE) recorded a significantly lower total weed count as compared to other treatments. Parameters related to blackgram growth and yield, including plant height, number of branches per plant, pods per plant, grain yield and haulm yield were significantly higher with weed-free treatment followed by application of imazethapyr 75 g/ha as PE fb HW at 30 DAS, remaining at par with application of pendimethalin 1.0 kg/ha + imazethapyr 75 g/ha (PE) and pendimethalin 1.0 kg/ha as PE fb HW at 30 DAS. The most economically efficient weed control method was the combination of pendimethalin 1.0 kg/ha + imazethapyr 75 g/ha (PE), as it resulted in the highest net returns and marginal benefit-cost ratio.

Application of Ethephon Manually or via Drone Enforces Bud Dormancy and Enhances Flowering Response to Chilling in Litchi (Litchi chinensis Sonn.)

Ethephon (2-chloroethylphosphonic acid) is frequently used for flush management in order to maximize flowering in litchi. However, the optimal dosage of ethephon, which balances between flush control effect and the detrimental effect on leaves, is unknown. This study aimed to identify the optimal ethephon dosage and test more efficient ethephon application methods, using a drone for flush control and flowering promotion in litchi. The effects of a single manual full-tree spray of 250, 500 or 1000 mg/L of ethephon in early November on the bud break rate, leaf drop rate, net photosynthetic rate, LcFT1 expression and floral induction (panicle emergence rate and panicle number) in ‘Jingganghongnuo’ litchi were examined in the season of 2021–2022. In the season of 2022–2023, the effects of drone application of 1000 mg/L of ethephon in early November on bud growth and floral induction were observed. The results showed that the manual ethephon treatments were effective at enforcing bud dormancy and elongating the dormancy period and that the effects were positively dependent on dosage. One manual spray of 1000 mg/L of ethephon in late autumn enabled a dormancy period of 6 weeks. The treatments advanced seasonal abscission of old leaves in winter and caused short-term suppression on photosynthesis within 2 weeks after treatment. Ethephon treatments, especially at 1000 mg/L, enhanced the expression of LcFT1 in the mature leaves and promoted floral induction reflected by earlier panicle emergence and increased panicle emergence rate and number in the terminal shoot. The floral promotion effect was also positively dosage dependent. The cumulative chilling hours below 15 °C from the date of treatment to the occurrence of a 20% panicle emergence rate were lowered in ethephon treatments. A drone spray of 1000 mg/L of ethephon solution consumed a sixth of the manual spray solution volume and was two thirds less effective in suppressing bud break compared with manual spraying. However, it achieved a significant flowering promotion effect comparable to traditional manual spraying. The results suggest that ethephon application enhanced flowering responsiveness to chilling as well as enforced bud dormancy. The application of ethephon with a drone proved to be an efficient method for flush control and flower promotion.

Differentiation of Vespa velutina nigrithorax Colonies Using Volatile Organic Compound Profiles of Hornets and Nests.

Vespa velutina (Lepeletier, 1836) (Hymenoptera: Vespidae) is a eusocial insect that lives in colonies of hundreds to thousands of individuals, which are divided into castes according to their task: queens, workers, and males. The proper functioning of the colony requires communication between the individuals that make up the colony. Chemical signals (pheromones) are the most common means of communication used by these insects to alarm and differentiate between individuals belonging or not to the colony. In this work, profiles of volatile organic compounds were obtained from the hornets and the external cover of four secondary nests located in the Basque Country. The obtained profiles were treated using chemometric tools. The grouping of hornets and nests according to the different colonies and geographical location was observed. In total, 37 compounds were found in common in hornets and nests. Most of them have been reported in the literature as belonging to different insects and plant species. This would corroborate the transfer of chemical compounds between the nest and the hornets' nest and vice versa. This information could be applied to the development of more efficient control methods for this invasive species, such as attractive traps or baits containing the relevant compounds.

Histological Investigations on Potato Tuber Moths, Phthorimaea operculella Treated with a Botanical Extract of Basil (Ocimum basilicum L), with Particular Attention to the Alterations in the Ultrastructure of the Larval Digestive Tract

Background: Phthorimaea operculella (Zeller), the potato tuber moth (PTM) is one of the most important pests that attacks potato, Solanum tuberosum L. crops worldwide. Adult moths attack tubers in fields as well as in storage. Infested tubers become unsuitable for trade or consumption, the tuber damage increases gradually after several generations of the pest during storage time. Botanicals have been found to be an efficient control method especially when they act as antifeedant or repellent, or even stomach poisons. The estimation of the mortal impact of some extracts against PTM was examined by emphasizing the Ultrastructure changes in the larval digestive tract by using scanning and transmission electron microscope. Results: Structural changes throughout the entire digestive tract (Fore-, Mid- and Hind-gut), induced by Basil extract were detected through both microscope, these changes leading lastly to the death of the treated larvae. Many different ruptures and malformations were detected throughout all the digestive tract assessment. Conclusions: Studies of the occurred changes revealed some histopathological changes through out all the digestive tract (Fore-, Mid- and Hind-gut), the attached muscular layer, tracheal system, and visceral fat bodies of the larvae when fed with tubers treated with Basil extract. From this findings, the use of plant extracts can be recommended for protection of potato tuber against the infestation by the tubers moths.

Highly Nonlinear Light-Nucleus Interaction.

The interaction between light and atomic nuclei is typically weak and confined within the linear and perturbative regime, which limits the achievable nuclear excitation probability and impedes potential applications such as nuclear optical clocks, nuclear lasers, and nuclear energy storage. In this Letter we show that the interaction between hydrogenlike thorium-229 ions (^{229}Th^{89+}) and contemporary intense lasers propels light-nucleus interaction into a highly nonlinear and nonperturbative regime. This interaction unlocks remarkably efficient nuclear excitation: over 10% of the ^{229}Th nuclei can be excited to the isomeric state by a single femtosecond laser pulse. Moreover, the laser-driven ^{229}Th^{89+} ions radiate multiple wavelengths of light that are high-order harmonics of the laser frequency, akin to the high harmonic generation process from laser-driven atoms but with distinct characteristics. These results pioneer a new frontier for exploring light-matter interaction, furnish a powerful method for efficient control over atomic nuclei, and pave a new way of nuclear coherent light emission.

Evaluation of Praziquantel effectiveness in treating Nile tilapia clinostomid infections and its relationships to fish health and water quality

This study aimed to conduct a multidisciplinary investigation integrating detailed morphology, molecular characterization, water parameters, histopathology alteration, and the trials of treatment of Clinostomum spp. In this study, 300 Nile tilapia (Oreochromis niloticus) were collected from the farmed and wild Nile River at Al Bahr Al Aazam, Giza Governorate to assess Clinostomid infection prevalence. Fish and water samples were collected from private fish farms, and water drains at Dakahlia, and Giza, Egypt. Analysis of the water revealed inadequate water quality, particularly in the fish farms. Snails and piscivorous birds were abundant at fish collection sites. The recovered Clinostomid MCs morphological characteristics and COI gene sequence analysis identified them as Clinostomum complanatum, C. phalacrocoracis, and Euclinostomum heterostomum. Clinostomid MCs disturbed the fish’s hematological and biochemical blood parameters. Bath treatment of parasitized fish with praziquantel (2 mg/L for 24 h) revealed a significant reduction in the number of vital MCs vs. infected fish (non-treated). Praziquantel (PZQ) is an effective and safe therapy for controlling Clinostomid infections affecting farmed Nile tilapia. The current findings indicate a link between poor environmental conditions and Clinostomum infections in tilapia. The study highlights the impacts of Clinostomid MCs on fish health and recommends bath treatment with PZQ as an efficient control method for these dangerous parasites to protect human and fish health.

Herbicidal weed management options for pigeon pea(Cajanus cajan) grown in southeastern Rajasthan

A field experiment was carried out during the kharif seasons of 2020–21, 2021–22 and 2022–23 at Kota, Rajasthan, to study the effect of post-emergence herbicides on productivity of pigeonpea [Cajanus cajan (L.) Millsp]. The results indicated that the minimum weed density and dry weight, and the highest yield attributes, yields, and net return of pigeonpea were obtained under weed free plot. All the herbicidal treatments significantly reduced the weed density and biomass compared to the weedy check. However, application of pendimethalin 30 EC @ 0.75 kg /ha (PE) fb Imazethapyr 10 SL @ 100 g/ha at 20–25 DAS + one inter-cultivation at 50 DAS recorded significantly higher plant height (1.82 m), pods/branch (22.96), pods/plant (206.3), weed control efficiency (84.39% at 60 DAS), grain yield (1,868 kg/ha), net return (` 87.66 x 103 /ha) and B: C ratio (3.35) over weedy check. This leads to the 61.73% higher pigeonpea grain yield over the weedy check plot. Hence it can be inferred that the ap- plication of pendimethalin 30 EC @ 0.75 kg /ha (PE) fb Imazethapyr 10 SL @ 100 g/ha at 20–25 DAS + one inter- cultivation at 50 DAS is an efficient weed control method for profitable cultivation of pigeonpea in south eastern Rajasthan.

Exploration of weak-PGML Method for Efficient Stability Control During Machining Operations

Stable control of the machining process can be difficult to maintain in production environments due to many factors, but especially uncertainty about selection of optimal machining parameters. The true underlying stability model for production machines on the shop floor is generally unknown, so parameters are typically selected by operators based on manufacturer recommendations or, in most cases, their accumulated shop floor experience. A class of methods referred to as physics-guided machine learning (PGML) offers a new approach for optimal parameter selection that incorporates theoretical knowledge about machining dynamics into the data-driven machine learning workflow. The focus of this paper is a version of PGML appropriate to shop floor operations in small and medium-sized machine shops (SMMs)—referred to as weak-PGML—that does not require a priori theoretical knowledge of machining dynamics to approximate the true underlying stability model for a particular machining scenario. Rather, the weak-PGML leverages the operator’s accumulated domain knowledge to uncover the true stability model and informs parameter selection. Weak-PGML reflects the limited resources and lack of technical expertise typical of most SMMs with legacy machines and a new generation of workforce without the accumulated experience of operators who have spent many years on the shop floor.

Characterization of Photobacterium damselae subsp. damselae isolated from diseased Asian seabass (Lates calcarifer) and the preliminary development of a formalin-killed cell vaccine.

Asian seabass (Lates calcarifer) is an economically important fish species that is widely cultivated in Thailand. However, aquaculture of Asian seabass is limited by infectious diseases. One of the most serious diseases is photobacteriosis, caused by Photobacterium damselae. Vaccination is recognized as an efficient disease prevention and pathogen control method for strengthening the aquaculture industry. To promote vaccine development, the characterization of pathogenic bacteria and their pathogenesis is required. In this study, isolates of P. damselae were obtained from commercial aquaculture farms in Thailand during 2019-2021. Analyses of 16S rRNA and the urease subunit alpha genes identified the isolates as P. damselae subsp. damselae (Phdd). Antibiotic susceptibility analyses showed that all Phdd isolates were resistant to amoxicillin (10 μg). Haemolysis and phospholipase activities were used to categorize P. damselae into three groups based on their biological activities. The pathogenicity of four candidates (SK136, PD001, PD002 and T11L) was tested in Asian seabass. Isolate SK136 showed the highest virulence, with a lethal dose (LD50) of 1.47 × 105 CFU/fish, whereas isolate PD001 did not show any virulence. Genotypic characterization, based on multi-locus sequence typing analysis, demonstrated that all candidates were novel strains with new sequence types (64, 65, 66 and 67). Preliminary vaccination using formalin-killed cells (FKCs) protected Asian seabass from artificial challenges. Taken together, these results provide fundamental knowledge for vaccine development against Phdd infection in Asian seabass.

Adaptive Current Angle Compensation Control Based on the Difference in Inductance for the Interior PMSM of Vehicles

Achieving good performance in terms of a fast and accurate maximum torque per ampere (MTPA) control method depends on both accurate parameter estimation and a sophisticated control strategy. However, it often requires a complex and long computational process. This paper proposes an efficient control method using the relationship of torque and current angle for maximum torque per ampere control of the interior permanent magnet synchronous motor (IPMSM) for vehicle application. It was found that it is not necessary for the control method to determine the inductance in the d-q axes; the identification of the difference between each axis is enough. Furthermore, this paper presents a simple and effective procedure to estimate the difference in inductance online, where the linear iron loss calculation method is also designed to support the above process. The proposed control method was experimentally validated on a 5 kW prototype by a TMS320F28335 microcontroller and DSPACE synchronized with a personal computer. The results show that the control process has faster and more accurate performance than the conventional method.

Efficient predictive control method for ORC waste heat recovery system based on recurrent neural network

The model predictive control performs well in regulating operating parameters and ensuring system safety when the organic Rankine cycles are operating with variable heat sources. However, traditional nonlinear predictive control based on the organic Rankine cycle mechanism model involves significant computational complexity, making it challenging to quickly find a control solution. This limitation hinders its application in the organic Rankine cycle for rapid response control. To address this issue, a fast model predictive control method is proposed in this work. A recurrent neural network model is well-trained using the input–output data of the organic Rankine cycle process, and it is used as a surrogate model in the design of the model predictive controller for the control of organic Rankine cycle operating parameters. The formulated optimal control problem is then transformed into a mixed integer linear programming problem, which can obtain high-quality and fast solutions during the control process. Through comparison with recurrent neural network-based nonlinear predictive control and pseudo-sequential method-based fast nonlinear predictive control, the results show that the designed controller can effectively accomplish the control of organic Rankine cycle operating parameters with smaller overshoot. Moreover, its average control solution time is shorter by 89.59% and 93.27% respectively while the total net output power of the system during the control process is 0.54% and 1.3% higher than that of the other two controllers. It exhibits superior control performance, even under variable waste heat conditions.

Improving Beef Cattle Production: Safety and Effectiveness of New Immunocastration Vaccine.

Reproductive control in mammals, particularly in beef production systems, is crucial for managing behaviors driven by sex steroids, which can cause biosecurity issues, reduced weight gain, and decreased meat quality. Additionally, controlling sexual activity in females prevents unwanted pregnancies when both sexes are housed together for fattening. Surgical castration in cattle, commonly performed under field conditions, is associated with significant welfare concerns due to pain and infection risks. Furthermore, castrating female cattle in the field is often impractically expensive. Hence, new reproductive control methods that prioritize animal welfare and are suitable for both sexes are essential. Immunocastration (IC), which involves vaccination against the GnRH-I hormone, has emerged as a promising alternative, demonstrating efficacy and safety in various species. Immunocastration has proven to be an effective alternative to surgical castration in controlling reproduction and promoting animal welfare in several species. This study aims to evaluate the safety, immunogenicity, immunocastration effect, and productive impact of a novel IC vaccine developed in Chile for female cattle. Two consecutive trials were conducted, the first under controlled conditions and the second under field conditions. The results demonstrated that the new vaccine is safe and effective for use in beef cattle, inducing specific immunity against GnRH-I, reducing gonadal functionality, and improving productive parameters. These findings suggest that this new IC vaccine can significantly benefit the beef cattle industry by providing a welfare-friendly and efficient method of reproductive control.

Hybrid Control Policy for Artificial Pancreas Via Ensemble Deep Reinforcement Learning.

The artificial pancreas (AP) has shown promising potential in achieving closed-loop glucose control for individuals with type 1 diabetes mellitus (T1DM). However, designing an effective control policy for the AP remains challenging due to the complex physiological processes, delayed insulin response, and inaccurate glucose measurements. While model predictive control (MPC) offers safety and stability through the dynamic model and safety constraints, it lacks individualization and is adversely affected by unannounced meals. Conversely, deep reinforcement learning (DRL) provides personalized and adaptive strategies but faces challenges with distribution shifts and substantial data requirements. We propose a hybrid control policy for the artificial pancreas (HyCPAP) to address the above challenges. HyCPAP combines an MPC policy with an ensemble DRL policy, leveraging the strengths of both policies while compensating for their respective limitations. To facilitate faster deployment of AP systems in real-world settings, we further incorporate meta-learning techniques into HyCPAP, leveraging previous experience and patient-shared knowledge to enable fast adaptation to new patients with limited available data. We conduct extensive experiments using the FDA-accepted UVA/Padova T1DM simulator across three scenarios. Our approaches achieve the highest percentage of time spent in the desired euglycemic range and the lowest occurrences of hypoglycemia. The results clearly demonstrate the superiority of our methods for closed-loop glucose management in individuals with T1DM. The study presents novel control policies for AP systems, affirming the great potential of proposed methods for efficient closed-loop glucose control.

The Application of Intelligent Technology in Interior Design and Its Impact on Living Experience

The development trend of intelligent residential interior design suggests that deep personalization, integration, greenness, and security will become mainstream. Designers will utilize AI technology to comprehensively understand user needs and craft unique smart home experiences. The future home environment will seamlessly integrate devices and offer efficient control methods for convenience. Additionally, there will be a heightened focus on environmental sustainability, optimizing energy usage through intelligent systems and potentially incorporating renewable energy sources to achieve zero-energy living spaces. Intelligent technology is significantly transforming interior design, guiding living spaces towards a smarter, more comfortable, and more sustainable future. Throughout this progression, design is evolving from a singular aesthetic focus to the fusion of technology and art. Designers are transitioning into technology leaders and artistic innovators, collectively shaping the future lifestyle of humanity.

Direct detection of ethyl carbamate in baijiu by molecularly imprinted electrochemical sensors based on perovskite and graphene oxide

Ethyl carbamate (EC), a carcinogen commonly found in Baijiu, requires an efficient detection method for quality control and monitoring. This study introduces a novel molecularly imprinted electrochemical sensor for sensitive and selective EC detection. We proposed a simple sol-gel method for the growth of perovskite-structured lanthanum manganate (LaMnO3) on graphene oxide (GO). A non-enzymatic electrochemical sensor was developed by coating a molecularly imprinted polymer synthesized via precipitation polymerization onto the surface of LaMnO3@GO. LaMnO3, with its superior three-dimensional nanocube structure, demonstrated excellent electrocatalytic activity, while the addition of GO provided a large specific surface area. The results indicate that the developed sensor exhibits exceptional recognition ability and electrochemical activity, which is attributed to the high affinity of LaMnO3@GO@MIP for EC. The sensor displays a broad linear range from 10 to 2000 μM, with a detection limit as low as 2.18 μM and long-term durability of 28 days. Notably, it demonstrates excellent selectivity, reproducibility, and stability even under different interference conditions. The sensor was successfully used to determine EC in real Baijiu samples. Overall, the sensor has broad application prospects for detecting trace contaminants in the field of food safety.

Learning-based robust output tracking control for unknown discrete-time nonlinear systems with dynamic uncertainty

Although numerous approximate dynamic programming (ADP) methods have been proposed and applied to robust control problems, research on the use of ADP methods for robust tracking control in nonlinear systems remains limited, especially for discrete time (DT) nonlinear systems. In this paper, we propose an efficient robust learning-based output tracking control method, referred to as r-LTC, for a class of unknown DT nonlinear systems with dynamic uncertainty. The objectives are achieved by: 1) using a system identification technique based on network-type coefficients Auto-Regressive with eXogenous variables (ARX) model to identify completely unknown systems; 2) reformulating the identified model into a state space model in terms of deviations from the reference trajectory, such that the robust tracking problem can be converted into a robust regulation problem; 3) transforming the robust regulation problem into an equivalent optimal regulation problem for a nominal system with a modified utility function; 4) alternately implementing the performance evaluation and control policy update through two neural networks (NNs) to numerically solve DT Hamilton-Jacobi-Bellman (HJB) equation, such that the approximated optimal feedback control inputs for nominal system can be obtained, allowing for realization of all unknown bounded uncertainties. The proposed method does not require a state observer and any steady state knowledge, only the measurable system input and output data are utilized. The convergence of NNs’ approximate weights and stability of the closed-loop uncertain systems are rigorously proven using the Lyapunov method. Lastly, a case study is performed using real data collected from the Shenzhen Metro Line 12 tunneling project to examine the performance of the proposed r-LTC algorithm in terms of the set value tracking accuracy, control system stability, and robustness against different types of disturbances.

Developing an SEIHR epidemiological model featuring saturated incidence rate and investigating its stability

Each and every person will inevitably come into contact with infectious diseases over their lifetime. This unavoidable phenomena is a characteristic of all people. Ideally, the effects of such diseases are still controllable, but under unfavourable circumstances, they may worsen and become full-fledged outbreaks. As a result, this article tries to offer an SEIHR model whose main goal is to reduce and stop the transmission of infectious diseases. Notably, infectious diseases continue to plague many countries, represented by the global threat of Covid-19, underlining the urgent need for efficient control methods. This model stands out because it carefully takes into account the saturated incidence rate of infectivity. The concept also divides the infected population into two different groups, enabling more focused and customized medical interventions. The article begins by closely examining the positivity and boundedness of the model, then conducts a thorough analysis of the fundamental reproductive number. The model's stability is then categorically proven at both equilibrium points. In summary, the article concludes with convincing numerical simulations that validate the suggested model, providing convincing evidence of its effectiveness as a whole. In conclusion, the suggested SEIHR model, which is supported by a wealth of empirical evidence confirming its efficacy and optimality, represents a significant advancement in the management and containment of infectious diseases. It offers a promising route for addressing the enduring threat of infectious diseases on a global scale, and its unique form and extensive analysis highlight its potential to alter our approach to infectious disease control.

Adaptive event‐triggered safety control for multiplayer mixed zero‐sum game with partial inputs delay

AbstractThis paper proposes an adaptive safety control method applicable to a multiplayer mixed zero‐sum (MZS) game of nonlinear systems with partial inputs delay. Firstly, a framework is introduced involving N players, where player 1 and player N form a zero‐sum (ZS) game, and player 1 and players 2 to N‐1 form nonzero‐sum (NZS) games, with some players experiencing time delays. Subsequently, the system's value function is augmented with a control barrier function (CBF) to ensure that the system's state remains within a safe region. Secondly, to approximate Nash equilibrium solutions, the study employs adaptive dynamic programming (ADP) and utilizes a critic‐only neural network (NN) to approximate optimal solutions. Diverging from traditional time‐trigger methods, computational and communication load reduction is achieved by introducing a state‐related event trigger condition. The stability of the system is then meticulously analyzed using the Lyapunov theorem. Finally, to validate the effectiveness of the proposed method, the study provides a simulation example demonstrating its performance. In summary, this research introduces an efficient adaptive safety control method for addressing multiplayer MZS games with partial inputs delay, incorporating CBFs, ADP, and state‐related event triggering.

Wind Power Station Efficiency Increase Methods Based on Wind Turbine Condition Process Mathematical Modelling when Distributing Electric Power Between the Consumers in Arctic and Far North Regions

The relevance of wind power plant efficient control method based on wind turbine condition process mathematical modeling is proved for electric power distribution between consumers, intended to minimize decision-making time on technological process redistribution of electric power production for another consumer by means of actual information on equipment failure time (wind turbine, electric power stocking system), that provides optimum power production and consumption planning in northern regions. The review of the basic mathematical modeling methods of electric power redistribution between consumers that showed there are certain problem statements with insufficient descriptions of mathematical models or the absence of ready and effective methods to solve them, in particular, wind turbine condition process mathematical modeling for electric power distribution to consumers in northern regions. As a result of the analysis of wind power plant efficient control method on the basis of wind turbine condition process mathematical modeling to distribute electric power to consumers in northern regions game theory and pulse system functioning mathematical methods application based on difference equations is expedient. The decision-making strategies stipulated by 'nature' (working capacity or wind turbine failure), the scheduler (repair or wind turbine redistribution) and a condition of phase co-ordinate (repair, idle, redistribution or operation wind turbine modes) are defined. On the basis of the accepted strategy and use of step function Heaviside F(t) for fixing of technological process time, the description of the wind turbine condition equation was made providing operative failure time information access on because due to idle time, redistribution or repair that provides optimum electric power production planning for a consumer.