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In the centralized heating, ventilating and air-conditioning (HVAC) system, air handling units (AHUs) are traditionally controlled by single-loop proportional-integral-derivative (PID) controllers. The control structure is simple, but the performance is usually not satisfactory. In this paper, we propose a cascade control strategy for temperature c...
Contexts in source publication
Context 1
... (AHU) is to transfer cooling load from air loop to chilled water loop by forcing air- flow over the cooling coil and into the space to be conditioned. The performance of AHU directly influences the performance of HVAC systems. Traditionally, AHUs are controlled by single- loop proportional-integral-derivative (PID) type controllers, as shown in Fig. 1, due to the relatively simple structure [1]. How- ever, in cases where requirements of the environment for equip- ments are very high, such as clean rooms (typically, temperature and humidity specifications require error tolerances of C and 2% RH, respectively), it would be desirable to integrate PID controllers into complex control ...
Context 2
... cooling only AHU system is shown in Fig. 1, it con- sists of cooling coil, air dampers, fans, chilled water pumps, and valves. The fresh air, depending on the outdoor air damper set- tings, may be mixed with the air passing through the recircula- tion air damper. Return air is drawn from the zones by the return fan and is either exhausted or recirculated, depending on the po- ...
Context 3
... test is conducted on a pilot centralized HVAC system, as shown in Fig. 8, where 1, 2, 3, and 4 indicate the compo- nents of computer controller, HVAC pilot plant, signal process board, and signal transmission cable, respectively. The system has three chillers, three zones with three AHUs, three cooling towers, and flexible partitions with up to 12 rooms. All motors (fans, pumps, and compressors) are controller by VSDs. The ...
Citations
... A three-layer BP neural network comprising a input neurons b hidden neurons and c output neurons is depicted in Figure 5 [14]. The threshold of the ith neuron in the hidden layer is δ i and the weight from the input layer to the hidden layer is w ij . ...
... A three-layer BP neural network comprising a input neurons b hidden neurons , and c output neurons is depicted in Figure 5 [14]. The threshold of the ith neuron in the hidden layer is δi and the weight from the input layer to the hidden layer is wij. ...
A 10 kV distribution network is a crucial piece of infrastructure to guarantee enterprises’ and households’ access to electricity. Stripping cables is one of many power grid maintenance procedures that are now quickly expanding. However, typical cable-stripping procedures are manual and harmful to workers. Although numerous automated solutions for grid maintenance have been created, none of them focus on cable stripping, and most of them have large dimensions to guarantee multi-functions. In this paper, a new cable-stripping robot for the 10 kV power system is introduced. The design of a live working cable-stripping robot that is appropriate for installing insulating rods is introduced, taking into account the working environment of 10 kV overhead lines and the structural characteristics of overhead cables. The robot is managed by an auxiliary remote control device. A cascade PID control technology based on the back propagation neural network (BPNN) method was developed, as the stripper robot’s whole system is nonlinear and the traditional PID controller lacked robustness and adaptability in complex circumstances. To validate the structural feasibility of the cable-stripping robot, as well as the working stability and adaptability of the BPNN–PID controller, a 95 mm2 cable-stripping experiment are carried out. A comparison of the BPNN–PID controller with the traditional PID method revealed that the BPNN–PID controller has a greater capacity for speed tracking and system stability. This robot demonstrated its ability to replace manual stripping procedures and will be used for practical routine power maintenance.
... Control structure is one of the most significant parts for electrical machine drive system, which affect the operation performance. The most common used control structure of electrical machine drive system is two-stage or three-stage cascade control structure (see [4][5][6]). To be specific, the starter/generator of gas turbine engine always adopts twostage proportional-integral (PI) cascade control to realize the speed tracking control or torque tracking control in starter mode and set-point voltage control in generator mode [7]. ...
... In order to simplify the analysis of cascade control, when f 1 = 0 and f 2 = 0, (3) can be generalized to be described by (5), (6). (4) can be generalized to be described by (7), (8) and (9). Thus, a ideal second-order cascade system and a ideal third-order cascade system, illustrated in Figure 1 and Figure 2, are ideal cascade integral systems. ...
A class of generalized proportional-integral-derivative (PID) control with feedforward compensation (FFC) is obtained for a class of cascade integral systems by equivalence analysis of the cascade control and such the analysis is obtained by using the information of the model and outermost loop feedback. Firstly, a new type of error related to the traditional error such as proportional (P) or proportional integral (PI) is given. Secondly, by analysis of cascade control for a class of ideal cascade integral systems, the generalized PID control with FFC is presented based on the proposed new type of error. Then, the generalized PID control with FFC is extended to a class of non-ideal cascade integral systems to reduce the number of feedback loop and sensors. Finally, the simulation results of the speed/position servo system of direct current motor are given to verify the theoretical analysis results.
... Neither MPC nor cascaded control, however, has seen widescale testing in real building systems. Numerous studies have implemented cascaded controllers in simulation with simple models of HVAC systems or with experimental test rigs [13]. In [14], a Hybrid Expansion Valve (HEV) was used to linearize the response of a small laboratory vapor compression cycle system. ...
Actuator hunting is a widespread and often neglected problem in the HVAC field. Hunting is typically characterized by sustained or intermittent oscillations, and can result in decreased efficiency, increased actuator wear, and poor setpoint tracking. Cascaded control loops have been shown to effectively linearize system dynamics and reduce the prevalence of hunting. This paper details the implementation of cascaded control architectures for Air Handling Unit chilled water valves at three university campus buildings. A framework for implementation the control in existing Building Automation software is developed that requires only a single line of additional code. Results gathered for more than a year show that cascaded control not only eliminates hunting in control loops with documented hunting issues, but provides better tracking and more consistent performance during all seasons. A discussion of efficiency losses due to hunting behavior is presented and illustrated with comparative data. Furthermore, an analysis of cost savings from implementing cascaded chilled water valve control is presented. Field tests show 2.2–4.4% energy savings, with additional potential savings from reduced operational costs (i.e., maintenance and controller retuning).
... The literature focuses on the modeling of HVAC systems, the performance of different controllers in HVAC control models, and economic analysis of energy savings. Guo et al. (2007) propose a cascade control strategy for AHU systems involving chilled water flow rate regulation, thereby enhancing the performance of AHUs. A neural network-based controller is developed for the outer control loop to enhance the robustness of the control system. ...
Occupancy-based strategies for the control of ventilation systems in buildings are effective for achieving energy savings and user comfort. Savings in energy consumption of more than 50% can be achieved by controlling heat, ventilation, and air conditioning (HVAC) systems with accurate sensory and occupancy information. In this study, the flow through the damper of the variable area valve (VAV) system and the speed of the blower’s variable frequency drive (VFD) are controlled in the HVAC system, on the basis of human occupancy and indoor parameters, namely, temperature and humidity, segment-wise in the building. In the proposed model, the flapper angle of the VAV is estimated using the indoor temperature, external temperature, and number of occupants. The occupancy data are fed to the controller proposed to regulate the flow through the ducts of the system, which is based on the flapper angle of the VAV, in order to maintain human comfort. The proposed scheme makes it possible to detect abnormalities in energy utilization and to trace maximum utilization in the building based on occupancy, with the control parameters of the HVAC adjusted for a comfortable indoor environment. Performance evaluation of the VAV system with its proposed control strategy, temperature, and flow distribution is simulated using Fluent software. A laboratory grade prototype incorporating the proposed control strategy is then developed, tested under three different conditions, and the results are reported. The experimental results show that an energy saving of 18% can be achieved.
... Depending on the heating/cooling requirement, an AHU can be operated in different operational modes namely: active cooling, economizer and active heating to ensure energy efficient operation. Conventionally, split range sequencing [2] and cascade control [3] methods have been used for the control design of the AHUs. However, the complex interaction between different AHU components combined with the high energy usage necessitate the development of advanced control techniques to enable optimal operation of the AHUs. ...
... Due to the negligible heat interaction with the surrounding, the mixing process is assumed to be adiabatic. The energy balance leads to (3). ...
Heating Ventilation and Air Conditioning (HVAC) systems are one of the main consumers of electrical energy. Being an essential component in HVAC systems, optimal operation of the air handling units (AHUs) can result in significant energy savings. In this paper, we study the modeling and optimal control of AHUs which can be operated in different modes (active cooling, economizer and active heating) to regulate the zone temperature within the desirable range. A simple control oriented model based on mass and energy balances is presented and validated against the operational data. A non-linear optimization program is formulated which accounts for different operational modes to enable optimal operation of the AHU. The proposed framework can be effectively applied to different AHU setups. An illustrative example is provided to show the effectiveness of the proposed approach.
... Similarly, in another study on optimizing network controller strategy, the back-propagation (BP) and particle swarm optimization (PSO) algorithms were implemented to tune the network controller weights for better control of the temperature [56]. Guo et al., [57], implemented performance function of mean squared error to tune the network weights of the HVAC heat medium prescient model in optimizing the energy utilization and thermal comfort of occupants. The HVAC system response is very important to be controlled because poor control of response will cause discomfort to occupants within the working environment and results in wastage of electricity. ...
The paper illustrates the review on the optimizations studies of HVAC systems based on three main methods – HVAC operational variables optimization, optimization of control parameters in HVAC system and parameter optimization in building models. For the HVAC system’s operational variables, the optimization process is based on the common and prescient energy utilization models. Thus, by comparing both, the non-common HVAC system models can get better output of energy reduction. Based on most of the studies, the occupancies thermal comfort requirements, are represented by the indoor air quality (IAQ) or the predicted mean vote (PMV) indexes. Comparing both requirements, the PMV index had a better overall energy reduction output of 47% and estimated annual energy reduction of 2,769 kg/year. Meanwhile, in optimization of HVAC’s control parameters, its overall aim is to achieve a better response output of the HVAC system in order to prevent energy wastage. Among this different optimization’s controller, the fuzzy logic tuning optimization has a better overall energy reduction. On the other hand, the parameter optimization in building model approach is performed before the construction of the structure itself, where multiple construction parameters are considerations in the design. In overall, when different tools for building parameter and model optimization are compared, the EXRETopt by using PMV comfort index approximately reduces 62% of the energy utilization.
... Cascade control structure (CCS) was first introduced by Franks and Worley (1956). Since then, it has been extensively used in the process industries, mainly in level, temperature, pressure and flow control loops (Campos-Rodríguez et al., 2019;Guo et al., 2007;Ranganayakulu et al., 2020). With the use of additional sensors and controllers, the effect of disturbance on process variables is reduced significantly (Alfaro et al., 2009). ...
Purpose
This paper aims to present an efficient and simplified proportional-integral/proportional-integral and derivative controller design method for the higher-order stable and integrating processes with time delay in the cascade control structure (CCS).
Design/methodology/approach
Two approaches based on model matching in the frequency domain have been proposed for tuning the controllers of the CCS. The first approach is based on achieving the desired load disturbance rejection performance, whereas the second approach is proposed to achieve the desired setpoint performance. In both the approaches, matching between the desired model and the closed-loop system with the controller is done at a low-frequency point. Model matching at low-frequency points yields a linear algebraic equation and the solution to these equations yields the controller parameters.
Findings
Simulations have been conducted on several examples covering high order stable, integrating, double integrating processes with time delay and nonlinear continuous stirred tank reactor. The performance of the proposed scheme has been compared with recently reported work having modified cascade control configurations, sliding mode control, model predictive control and fractional order control. The performance of both the proposed schemes is either better or comparable with the recently reported methods. However, the proposed method based on desired load disturbance rejection performance outperforms among all these schemes.
Originality/value
The main advantages of the proposed approaches are that they are directly applicable to any order processes, as they are free from time delay approximation and plant order reduction. In addition to this, the proposed schemes are capable of handling a wide range of different dynamical processes in a unified way.
... In the reviewed literature, several papers were identified investigating methods to improve the stability of supply air temperature and airflow control without a formal mathematical optimization process (Seem, Park, and House 1999;Seem et al. 2000;Xu, Wang, and Shi 2004;Guo, Song, and Cai 2007;Moradi, Saffar-Avval, and Bakhtiari-Nejad 2011). For example, Seem, Park, and House (1999) investigated improving the stability of a conventional split-range sequencing strategy by introducing state transition delays between the aforementioned four modes of AHU operation. ...
... Wang and Xu (2004) also investigated the compatibility of a similar freezing and gain scheduling approach with a CO 2 -based demand-controlled ventilation strategy. Guo, Song, and Cai (2007) presented a neural network-assisted cascade control system to account for the impact of chilled water rate fluctuations on supply air temperature control. They experimentally demonstrated the method's ability to improve supply temperature setpoint tracking and stability. ...
Operational parameters of air handling units (AHUs) play an important role in the energy and comfort performance of commercial buildings. Current guidelines to determine these parameters are based on heuristics and are not informed by formal optimization. This paper presents a building performance optimization method to derive sequence of operations for multi-zone AHUs. To this end, 27 variants of a generic EnergyPlus office building model are built representing three levels of occupancy, envelope, and HVAC capacity scenarios. The supply temperature, morning start time, and economizer settings of the model are optimized for the 27 scenarios by using a genetic algorithm. The results highlight that optimal supply temperature setpoints transition from ∼12°C to a range of ∼16°C to ∼20°C over an outdoor temperature range of ∼20°C to ∼0°C. Energy savings are estimated as ∼20% relative to a reference case with a constant supply temperature and preheating/precooling period.
... Regarding this case, the HVAC control system can only cope with this problem by adding pre-cooling coils or reheating coils [23], and the reheating coils are not recommended when trying to save energy. Furthermore, previous studies [24] did not compare the backpropagation algorithm of online tuning performance to other algorithms, even though the backpropagation algorithm has certain deficits (e.g., the backpropagation algorithm suffers from two main drawbacks: network paralysis (forcing the neurons to operate atlarge outs) and trapping at the local minima [25]). ...
... Then, continuing iteration as long as the error changes to get the set of sequence of the ΔS (step-length), in order to form optimal path of the tangent to the graph of the error equation, and thus by substituting the values of the parameters and weights vector [ i Δa i Δb i ] T of Eq. (23) into Eq. (24). The recursive Equation reaches a terminating condition by setting the error equal to zero, the following steps will be followed to achieve the algorithm goal. ...
In some fields, such as the semiconductor manufacturing process, museum, pharmaceutical, and medicine manufacturing industry, the HVAC system needs a very fast response time to protect products and more energy-efficient buildings than traditional controllers. So, the proposed controller is designed to overcome such problems by using integrated fuzzy PI-PD Mamdani-type (FPIPDM) and cluster adaptive training based on Takagi-Sugeno-Kang (CABTSK) type. The spans of the fuzzy membership functions of the FPIPDM are tuned online by the Nelder-Mead simplex search (NMSS) algorithm to minimize time response, while the CABTSK model is tuned offline and online using a gradient descent (GD) algorithm to enhance the stability of the overall system and reject disturbances. Then, the integration framework is used to enable the concept of time-optimal based on the bang-bang code delegation. In this sense, a selected switch delegates the execution of proper control code to the action processor that provides computational resources to control indoor conditions. The predicted mean vote (PMV) index provides a higher comfort level than the temperature, as it considers six variables related to thermal comfort. The results of the proposed structure show that it improves the overall output accuracy and significantly reduces the response time. Furthermore, it increases the robustness of the indoor conditions and it is quite applicable to the MIMO HVAC systems processes with strong coupling actions between temperature and humidity, large time delay, noise, disturbances, nonlinearities, and imprecise identification model.
... Mathematical relation used in the ventilation control mechanism based on the differential gas concentration is shown in Equation 1 [20]. Guo et al. [25] proposed a Neural Network (NN) based control system for air handling unit. The authors used PID and NN controller in cascade mode. ...
... Modified air quality controlling mechanism based on fresh air supply and exhaust in the building[20]-[ 25]. ...
In this review paper we have discussed the existing state-of-the-art practices of improved intelligent features, controlling parameters and Internet of things (IoT) infrastructure required for smart building. The main focus is on sensing, controlling the IoT infrastructure which enables the cloud clients to use a virtual sensing infrastructure using communication protocols. The following are some of the intelligent features that usually make building smart such as privacy and security, network architecture, health services, sensors for sensing, safety and overall management in smart buildings. As we know, the internet of things (IoT) describes the ability to connect and control the appliances through the network in smart buildings. The development of sensing technology, control techniques and IoT infrastructure give rise to a smart building more efficient. Therefore, the new and problematic innovation of smart buildings in the context of IoT is to a great extent and scattered. The conducted review organised in a scientific manner for future research direction which presents the existing challenges, and drawbacks.
