292 research outputs found

    Proportional-Integral Extremum Seeking for Optimizing Power of Vapor Compression Systems

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    Conventionally, online methods for minimizing power consumption of vapor compression systems rely on the use of physical models. These model-based approaches attempt to describe the influence of commanded inputs, disturbances and setpoints on the thermodynamic behavior of the system and the resultant consumed electrical power. These models are then used online to predict the combination of inputs for a measured set of thermodynamic conditions that both meets the heat load and minimizes power consumption. However, these models of vapor compression systems must contain nonlinear terms of sufficient complexity in order to accurately describe the region near the optimum operating point(s), but also must rely on simplifying assumptions in order to produce a mathematically tractable representation. For these reasons, model-based online optimization of vapor compression machines have not gained traction in application, and have created an opportunity for model-free techniques such as extremum seeking control, which is gradient descent optimization implemented as a feedback controller. While traditional perturbation-based extremum seeking controllers for vapor compression systems have proven effective at minimizing power without requiring a process model, the algorithm\u27s requirement for multiple distinct timescales has limited the applicability of this method to laboratory tests where boundary conditions can be carefully controlled, or simulation studies with unrealistic convergence times. Perturbation-based extremum seeking requires that the control input be manipulated with a time constant approximately two orders of magnitude slower than the slowest vapor compression system dynamics, otherwise instabilities in the closed loop system occur. As a result, convergence to the optimum for slow processes such as thermal systems is restrictive due to inefficient estimation of the gradient, and slow (integral-action dominated) adaptation in the extremum seeking control law. In order to address this timescale separation issue, we have previously developed an algorithm called ``time-varying extremum seeking that more efficiently estimates the gradient of the performance metric and applied this algorithm to the problem of setpoint optimization for compressor temperatures. That algorithm improved the convergence rate to one timescale slower than the vapor compression machine dynamics. In this paper, we optimize power consumption through the application of a newly-developed proportional--integral extremum seeking controller (PI-ESC) that converges at the same timescale as the process. This method uses the improved gradient estimation routines of time-varying extremum seeking but also modifies the control law to include terms proportional to the estimated gradient. This modification of the control law, in turn, requires a revision to the gradient estimator in order to avoid bias. PI-ESC is applied to the problem of compressor discharge temperature selection for a vapor compression system so that power consumption is minimized. Because of the improved convergence properties of PI-ESC, we show that optimum values of discharge temperature can be tracked in the presence of realistic disturbances such as variation in the outdoor air temperature---enabling application of extremum seeking control to vapor compression systems in environments where previous methods have failed. The method is demonstrated experimentally on a 2.8 kW split ductless room air conditioner and in simulation using a custom-developed Modelica model

    Comparing Realtime Energy-Optimizing Controllers for Heat Pumps

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    As the vapor compression machine has become more sophisticated (for example, through the adoption of variable speed compressor technology, electronic expansion valves and variable speed fans), the opportunities to improve efficiency are increasingly realized through the control algorithms that operate machine actuators. However, designing control algorithms that minimize energy consumption is not straightforward: the heat load disturbances to be rejected are not measured, the governing dynamics are nonlinear and interactive, and the machine exhibits strong coupling between the multivariate inputs and outputs. Further, many heat pumps must also operate in cooling mode, forcing compromises in sensor locations and actuator selection. This paper compares two controllers for realtime (online) energy optimization of heat pumps. The first energy-optimizing controller is model-based. A custom multi-physical model of the dynamics of a heat pump is developed in the Modelica modeling language and used to obtain the relationship between control inputs and power consumption as a function of the operating conditions. The gradient of this relationship is computed symbolically and used to derive a gradient descent control law that is shown to drive actuator inputs such that the system power consumption is minimized. To address the concern of modeling error on optimization performance, the controller based on a model of a heat pump will be tested on a physical system in an experimental setting for the submitted paper. We expect the convergence rate to be exponential, and will quantify the sensitivity between modeling errors and the non-optimality of the stabilized system. The second approach is model-free and based on the authors\u27 time-varying (TV) and proportional-integral (PI) extremum seeking control (ESC) algorithms. Briefly, extremum seeking controllers use an estimate of the gradient between a plant\u27s manipulated inputs and an objective signal (i.e., power consumption) to steer the system toward an optimum operating point, under the assumption that this relationship is convex. Whereas traditional ESC methods exhibit slow and non-robust convergence, our TV-ESC and PI-ESC methods have demonstrated higher performance due to the estimation routine that tracks the gradient as a time-varying parameter. We expect this algorithm to converge faster than transitional perturbation-based ESC methods (as we have previously demonstrated), but perhaps slower than the model-based approach. However, we expect this controller to converge to a neighborhood around the true optimum since modeling errors are not applicable in this model-free algorithm. The final paper will compare convergence properties of these two methods through experiments obtained on a commercial four-zone heat pump installed in calorimetric-style testing chambers, and the resultant coefficients-of-performance (COPs) will be measured

    Extremum Seeking Control for An Air-source Heat Pump Water Heating System with Flash Tank Cycle based Vapor Injection

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    Vapor injection (VI) techniques have been well received as an effective technology for improving the performance of air-source heat pump (ASHP) under very low ambient temperature, for which the flash tank cycle (FTC) and the internal heat exchanger cycle (IHXC) are the two majaor configurations. In principle, FTC has higher achievable performance than IHXC because that the saturated vapor from the flash tank has a lower temperature which helps reduce the compressor discharge temperature and thus power consumption [1]. However, development of the FTC technology has been hampered by the lack of proper control/operational strategy that can optimize the thermodynamic characteristics of the vapor injection channel under variable ambient and load conditions. In the flash tank, the refrigerant is separated into the liquid and saturated vapor phase. The liquid refrigerant enters the lower-stage expansion valve and then circulates through the evaporator before entering the suction side of compressor, while the saturated-vapor refrigerant is injected into the intermediate pressure port of compressor. As saturated vapor is in principle the best choice for the vapor injection channel, superheat adjustment via the upper electronic expansion valve (EEV) is no longer viable. A liquid level measurement for the flash tank has been considered as feedback for the EEV control, however, determination of the optimum liquid level is rather difficult for practical operation due to the complexity of the underlying process and diversity in operating condition. In this paper, we propose an extremum seeking control (ESC) based strategy for efficient operation of the FTC-VI based ASHP heating systems [3]. ESC is a model-free real-optimization strategy, which is a dynamic gradient search with the online gradient estimation realized by a dither-demodulation scheme. For this problem, the setpoint for the intermediate pressure of injected saturated vapor is adopted as the manipulated input of the ESC, which is adjusted by the opening of the upper EEV via an inner-loop proportional-integral (PI) controller; while the total power consumption of the system is the only feedback needed. The heating load is regulated by the compressor capacity. To evaluate the proposed ESC strategy, a Modelica based dynamic simulation model of an FTC-VI based ASHP water heater is developed with Dymola and TIL Library. The hot-water outlet temperature is regulated by the compressor capacity, while the upper-EEV opening is used to regulate the intermediate pressure and liquid level of the flash tank. Simulation study is performed under different scenarios of ambient and thermal load conditions. The results show that the ESC is able to find the optimum intermediate pressure (corresponding to the optimum flash tank liquid level) by adjusting the upper EEV, which minimizes the total power consumption without sacrifice of heating load regulation and thus maximizes the system COP. To the authors’ best knowledge, the proposed strategy is a novel control solution to the optimal operation of FTC-VI ASHP systems, which does not require plant models or sensor measurements beyond power consumption. The presented results promises a great potential for the proposed strategy to facilitate the adoption of FTC technology

    Investigation of Some Self-Optimizing Control Problems for Net-Zero Energy Buildings

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    Green buildings are sustainable buildings designed to be environmentally responsible and resource efficient. The Net-Zero Energy Building (NZEB) concept is anchored on two pillars: reducing the energy consumption and enhancing the local energy generation. In other words, efficient operation of the existing building equipment and efficient power generation of building integrated renewable energy sources are two important factors of NZEB development. The heating, ventilation and air conditioning (HVAC) systems are an important class of building equipment that is responsible for large portion of building energy usage, while the building integrated photovoltaic (BIPV) system is well received as the key technology for local generation of clean power. Building system operation is a low-investment practice that aims low operation and maintenance cost. However, building HVAC and BIPV are systems subject to complicated intrinsic processes and highly variable environmental conditions and occupant behavior. Control, optimization and monitoring of such systems desire simple and effective approaches that require the least amount of model information and the use of smallest number but most robust sensor measurements. Self-optimizing control strategies promise a competitive platform for control, optimization and control integrated monitoring for building systems, and especially for the development of cost-effective NZEB. This dissertation study endorses this statement with three aspects of work relevant to building HVAC and BIPV, which could contribute several small steps towards the ramification of the self-optimizing control paradigm. This dissertation study applies self-optimizing control techniques to improve the energy efficiency of NZEB from two aspects. First, regarding the building HVAC efficiency, the dither based extremum seeking control (DESC) scheme is proposed for energy efficient operation of the chilled-water system typically used in the commercial building ventilation and air conditioning (VAC) systems. To evaluate the effectiveness of the proposed control strategy, Modelica based dynamic simulation model of chilled water chiller-tower plant is developed, which consists of a screw chiller and a mechanical-draft counter-flow wet cooling tower. The steady-state performance of the cooling tower model is validated with the experimental data in a classic paper and good agreement is observed. The DESC scheme takes the total power consumption of the chiller compressor and the tower fan as feedback, and uses the fan speed setting as the control input. The inner loop controllers for the chiller operation include two proportional-integral (PI) control loops for regulating the evaporator superheat and the chilled water temperature. Simulation was conducted on the whole dynamic simulation model with different environment conditions. The simulation results demonstrated the effectiveness of the proposed ESC strategy under abrupt changes of ambient conditions and load changes. The potential for energy savings of these cases are also evaluated. The back-calculation based anti-windup ESC is also simulated for handling the integral windup problem due to actuator saturation. Second, both maximum power point tracking (MPPT) and control integrated diagnostics are investigated for BIPV with two different extremum seeking control strategies, which both would contribute to the reduction of the cost of energy (COE). In particular, the Adaptive Extremum Seeking Control (AESC) is applied for PV MPPT, which is based on a PV model with known model structure but unknown nonlinear characteristics for the current-voltage relation. The nonlinear uncertainty is approximated by a radial basis function neural network (RBFNN). A Lyapunov based inverse optimal design technique is applied to achieve parameter estimation and gradient based extremum seeking. Simulation study is performed for scenarios of temperature change, irradiance change and combined change of temperature and irradiance. Successful results are observed for all cases. Furthermore, the AESC simulation is compared to the DESC simulation, and AESC demonstrates much faster transient responses under various scenarios of ambient changes. Many of the PV degradation mechanisms are reflected as the change of the internal resistance. A scheme of detecting the change of PV internal shunt resistance is proposed using the available signals in the DESC based MPPT with square-wave dither. The impact of the internal resistance on the transient characteristics of step responses is justified by using the small-signal transfer function analysis. Simulation study is performed for both the single-string and multi-string PV examples, and both cases have demonstrated successful results. Monotonic relationship between integral error indices and the shunt internal resistance is clearly observed. In particular, for the multi-string, the inter-channel coupling is weak, which indicates consistent monitoring for multi-string operation. The proposed scheme provides the online monitoring ability of the internal resistance condition without any additional sensor, which benefits further development of PV degradation detection techniques

    Dynamic Charge Management for Vapor Compression Cycles

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    Because vapor compression air-conditioners and heat pumps consume significant amounts of electrical power in today\u27s residential and commercial buildings, energy optimization of these systems is becoming increasingly important from the perspectives of both environmental conservation and economic value. Corresponding efforts to improve the energy efficiency of these machines require attention to all stages of system design, installation, and operation, due to the myriad factors influencing power consumption. Among the many variables that must be optimized, one particularly salient variable is the mass of refrigerant contained within the cycle, or the refrigerant charge; this variable is strongly coupled to many other variables in the system, including the electrical power consumption, the system pressures, and the degree of subcooling and superheat in the heat exchangers. As such, the mass of refrigerant in the system must be carefully tuned for a given set of operational conditions to maximize the system\u27s energy efficiency. In practice, field-installed vapor compression systems are often not charged with the mass of refrigerant that optimizes energy efficiency for the conditions in which systems actually operate. In accordance with the conventional view of the refrigerant charge as a static system parameter, the mass of refrigerant is often specified to maximize the average energy efficiency over a set of multiple conditions. This approach results in suboptimal energy efficiency at any one of the conditions within the rating set, and furthermore often results in lower energy efficiency at non-rated conditions. Such an impact is especially evident in reversible heat pump cycles because the optimal refrigerant mass for a cycle over a range of conditions in cooling mode is often very different than the optimal refrigerant mass in heating mode. As today\u27s system manufacturers sell equipment across large geographic ranges with a wide range of ambient conditions and operational requirements, the cumulative impact of operating these systems with suboptimal refrigerant charge is generally a much higher rate of energy consumption than would be observed with cycles that incorporate an optimally specified refrigerant charge. In this paper, we describe a system architecture for a vapor compression system that enables the circulating refrigerant charge to be modulated as a function of time, effectively allowing the refrigerant charge to be optimized for a predicted or observed set of operational conditions. This is accomplished by dynamically controlling the amount of refrigerant sequestered in a storage vessel (referred to as a dynamic receiver) that is continuously coupled to the other components of the system. We first explore alternate system architectures that have been previously proposed for similar purposes, and elaborate on the opportunities that are afforded by this particular candidate architecture. A set of first-principles physics-based dynamic models are then developed using the Modelica language, and a candidate controller architecture is discussed that directly optimizes the electrical power consumption by using this new dynamic receiver. Finally, we will compare energy performance of this proposed system with that of conventional system architectures to evaluate its benefits over a range of operational conditions

    Study of different subcooling control strategies in order to enhance the performance of a heat pump

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    [EN] The performance of vapor-compression systems working with subcritical refrigerants varies with the degree of subcooling. There is an optimal subcooling that maximizes efficiency. However, it depends on the operating conditions and the control of the system needs to be adapted. Most of the works available in literature are able to operate in optimal conditions only at the design point or if a system is designed to be able to adapt its subcooling, only complex control algorithms that usually are difficult to set and time-costly, are used. This work focuses on the study of the main variables influencing the optimal subcooling and analyzes two different control methodologies from the theoretical point of view. Based on the theoretical study a final control strategy is selected and tested experimentally. The reliability, stability and robustness of the selected strategy are experimentally demonstrated for a wide set of operating conditions. (c) 2018 Elsevier Ltd and IIR. All rights reserved.Part of the work presented was carried by Estefania Hervas Blasco with the financial support of a PhD scholarship from the Spanish government SFPI1500 x 074478XV0. The authors would like also to acknowledge the Spanish 'MINISTERIO DE ECONOMIA Y COMPETITIVIDAD', through the project. "MAXIMIZACION DE LA EFICIENCIA Y MINIMIZACION DEL IMPACTO AMBIENTAL DE BOMBAS DE CALOR PARA LA DESCARBONIZACION DE LA CALEFACCION/ACS EN LOS EDIFICIOS DECONSUMO CASI NULO" with the reference ENE2017-83665-C2-1-P for the given support.Hervas-Blasco, E.; Pitarch, M.; Navarro-Peris, E.; Corberán, JM. (2018). Study of different subcooling control strategies in order to enhance the performance of a heat pump. International Journal of Refrigeration. 88:324-336. https://doi.org/10.1016/j.ijrefrig.2018.02.003S3243368

    Experimental Evaluation for an Extremum Seeking Control Strategy based on Input-output Correlation with a Mini-split Air Conditioning System

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    Extremum Seeking Control (ESC) has emerged as a model-free real-time optimization framework, typically based on dither-demodulation driven gradient estimation. However, such conventional ESC suffers from slow convergence. Salsbury et al. have recently proposed an input-output correlation based ESC (IOC-ESC) strategy anchored on a statistical analysis. The IOC-ESC algorithm is less sensitive to changes in its internal parameters because of the use of a normalized correlation coefficient in the feedback loop. The design goal of the algorithm is to have only two tunable parameters: (1) a time scale parameter that relates to the time open loop time constant of the system; and (2) the amplitude of the dither signal. A suitable set of generic internal parameters is still in the process of being identified as more test data become available from different system types. For the work reported here, the feedback gain (referred to as the tuning factor) with the IOC-ESC was also tuned for optimal performance. This study aims to conduct an experimental evaluation for the IOC-ESC strategy with a ductless mini-split air conditioning system, compared with conventional ESC (CON-ESC). The system features variable-capacity compressor operation and variable-speed operation for the evaporator and condenser fans. In this study, both single-input and two-input ESC scenarios are tested. The manipulated inputs include the evaporator and condenser fan speeds, while the total power consumption is used as feedback for all cases. The experimental setup is developed with a 9000 BTU variable-speed mini-split AC system serving a 4’x8’x6’ insulated chamber, and an electrical fan heater is used to provide an artificial heat load. The data acquisition and control algorithms are implemented on a National Instruments CompactRIO platform. Both IOC-ESC and CON-ESC are tested with the same setup. For single-input scenario, the manipulated input is the condenser fan speed. The testing results of five trials of IOC-ESC are used to evaluate the impact of the two tuning parameters, i.e. dither frequency and tuning factor, on the ESC performance. IOC-ESC#1, IOC-ESC#4 and IOC-ESC#5 have the same dither frequency but different tuning factors, while IOC-ESC#1, IOC-ESC#2 and IOC-ESC#3 have the same tuning factor but different dither frequencies. The testing results of two trials of CON-ESC are then compared with the IOC-ESC results. Both CON-ESC and IOC-ESC can effectively reduce the power consumption of the mini-split system without sacrificing zone temperature regulation. Moreover, the settling time of IOC-ESC ranges from 300 to 600 seconds, while the settling time of CON-ESC ranges from 900 to 1200 seconds. Overall, the IOC-ESC converges faster than the CON-ESC. For two-input scenario, the manipulated inputs are condenser fan speed and evaporator fan speed. The testing results of the two-input IOC-ESC are compared with the result of a two-input CON-ESC trial by Yan et al. with the same system. The settling times for CON-ESC and IOC-ESC are about 1800 and 1200 seconds, respectively. In summary, both CON-ESC and IOC-ESC can optimize the condenser fan speed and evaporator fan speed for energy efficient operation, while the IOC-ESC converges faster and has fewer tuning parameters

    Extremum seeking control of battery powered vapor compression systems for vehicles

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    This thesis investigates the real-time energy optimization of battery powered vapor compression systems (VCS) for vehicles. Battery powered VCS are critical for maintaining passenger comfort in engine-off situations, and are especially important to long-haul truck drivers who sleep inside their vehicle overnight. However, one drawback of battery powered vehicle VCS is their short lifespan which may not provide cooling through the whole night while the vehicle engine is turned off. One reason for short system lifespan is suboptimal input selection; the combination of inputs to the VCS often yields a power consumption higher than necessary to generate the required vehicle cooling. This thesis proposes the use of extremum seeking control (ESC), a class of real-time model-free optimization algorithms, to determine the optimal combination of system inputs that minimizes the VCS power consumption while meeting given objectives. In order to determine algorithm efficacy, we implemented three different ESC algorithms (perturbation-ESC, least squares-ESC and recursive least squares-ESC) on a simulated and physical integrated VCS (the VCS in conjunction with the battery pack and vehicle cabin). Simulation and experimental results demonstrate significant increases in energy efficiency and battery life through the use of these algorithms, with least squares-ESC and recursive least squares-ESC being the most effective of the three

    Modélisation dynamique et commande optimale d'un système de réfrigération à base d'éjecteur

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    Recently, the ejector-based refrigeration system (ERS) has been widely used in the cooling industry as an appropriate alternative to the compressor-based cooling systems. However, the advantages of ERS such as the reliable operation and low operation and maintenance costs are overshadowed by its low efficiency and design complexity. In this context, this thesis presents the efforts to develop a control model enabling the ERS to operate in its optimal operational conditions. The extensive experimental studies of ERS revealed that at a fixed condenser inlet condition, there exists an optimal primary stream mass flow rate (generating pressure) that simultaneously maximizes the compression ratio (Cr) and exergy efficiency and minimizes the evaporating pressure. Then, the steady state models of the heat exchangers were developed and used to investigate the influence of the increase in generating pressure on the coefficient of performance (COP) of the system and it showed that increasing the generating pressure reduces the COP, linearly. In order to predict the choking regime of the ejector and explain the reasons of observed physical phenomenon, the 1D model of a fixed geometry ejector installed within an R245fa ERS was developed. The developed model demonstrated that the ejector operates in the subcritical mode when the generating pressure is below the Cr optimum point, while it operates in critical mode at or above the optimum generating pressure. Next, a dynamic model of the ERS was built to evaluate the ERS transient response to an increase in the primary stream mass flow rate. Since the ERS dynamics is mainly dominated by the thermal dynamics of the heat exchangers, the dynamic models of the heat exchangers were developed using the moving boundary approach and connected to the developed models of the ejector and steady state models of the pump and expansion valve to build a single dynamic model of the system. The built dynamic model of an ERS was used to estimate the time response of the system in the absence of accurate experimental data of the system’s dynamics. Finally, a control model was designed to drive an ERS towards its optimal operation condition. A self-optimizing, model-free control strategy known as Extremum seeking control (ESC) was adopted to minimize evaporating pressure in a fixed condenser thermal fluid inlet condition. The innovative ESC model named batch phasor ESC (BPESC) was proposed based on estimating the gradient by evaluating the phasor of the output, in batch time. The simulation results indicated that the designed BPESC model can seek and find the optimum evaporating pressure with good performance in terms of predicting the steady state optimal values and the convergence rates.Récemment, le système de réfrigération à éjecteur (SRE) a été largement utilisé dans l'industrie du refroidissement en tant que solution de remplacement appropriée aux systèmes de refroidissement à compresseur. Cependant, les avantages du SRE, tels que le fonctionnement fiable et les faibles couts d'exploitation et de maintenance, sont éclipsés par son faible rendement et sa complexité de conception. Dans ce contexte, ce projet de recherche de doctorat a détaillé les efforts déployés pour développer une stratégie de commande permettant au système de fonctionner dans ses conditions opérationnelles optimales. Les études expérimentales approfondies du SRE ont révélé que, dans une condition d'entrée de condensateur constante, il existe un débit massique optimal du flux primaire (générant une pression) qui maximise simultanément le taux de compression (Cr) et l'efficacité exergétique, et minimise la pression d’évaporation. Ensuite, les modèles à l’état d’équilibre des échangeurs de chaleur ont été développés et utilisés pour étudier l’influence de l’augmentation de la pression générée sur le coefficient de performance (COP) du système et il en ressort que l'augmentation de la pression génératrice réduit le COP de manière linéaire. Afin de prédire le régime d'étouffement de l'éjecteur et d'expliquer les raisons du phénomène physique observé, le modèle 1D d'un éjecteur à géométrie fixe installé dans un système SRE R245fa a été développé. Le modèle développé a démontré que l'éjecteur fonctionne en mode sous-critique lorsque la pression génératrice est inférieure au point optimal de Cr, alors qu'il fonctionne en mode critique à une pression égale ou supérieure à la pression génératrice optimale. Ensuite, un modèle dynamique du SRE a été développé pour étudier la réponse transitoire du SRE lors d’une augmentation du débit massique du flux primaire. Puisque la dynamique du SRE est principalement dominée par la dynamique thermique des échangeurs de chaleur, les modèles dynamiques des échangeurs de chaleur ont été développés à l'aide de l'approche des limites mobiles et connectés aux modèles développés de l'éjecteur et des modèles à l'état stationnaire de la pompe et de la vanne un seul modèle dynamique du système. En l’absence de données expérimentales précises sur la dynamique d’un système SRE, le modèle dynamique développé du SRE a été simulé numériquement pour étudier sa réponse temporelle. Enfin, une stratégie de commande extrêmale (ESC) a été élaboré pour régler automatiquement le SRE à ses conditions de fonctionnement optimales, c’est-à-dire pour trouver la vitesse de la pompe qui minimise la pression dans des conditions d'entrée de condenseur fixes. Afin de proposer une ESC implémentable en temps discret sur une installation réelle sujette à un bruit de mesure important et un traitement hors-ligne par trame, une nouvelle commande extrémale basée sur une approche par phaseur avec une procédure de traitement de signal par trame (BPESC) a été développée et simulée avec le modèle numérique. Les résultats de la simulation ont indiqué que le modèle BPESC peut trouver la vitesse optimale de la pompe avec de bonnes performances en termes de précision et de vitesse de convergence
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