Knowledge Of Time Delay Research Articles

Enabling large-scale dynamic simulations and reducing model complexity of district heating and cooling systems by aggregation

District heating and cooling (DHC) systems are considered cornerstones of a future heat and cold supply due to their ability to integrate renewable and waste heat sources as well as long and short-term storage technologies and their flexibility for integration of other infrastructures. Therefore, DHC systems may represent the central hub of an interlinked overall energy system. These options nevertheless lead to a more complex system if implemented as the number of technical components and potential interactions increase and as the energy demands on the network grow. The transportation of the heat supplied to the consumers through water flow involves both time and temperature-dependent changes based on the mass flow rates and the heat losses to the surrounding from the pipes. The dynamic properties of the consumers and the distribution pipes strongly influence the operation of the district heating systems. Dynamic modeling tools are required to cope with the increasing complexities and to optimize the operation of the DHC systems by using the knowledge of time delay in the heat transport, temperature distribution and flow characteristics. Nevertheless, it is computationally challenging to simulate large-scale DHC networks dynamically. One proposed method for improvement in this context is aggregation, which simplifies the topological complexities of the original network by reducing the number of pipe junctions (branches) and pipes. This paper focuses on the analysis and evaluation of the application of two aggregation methods, the Danish and the German method, in a dynamic modelling environment and comparing the aggregation to the monitoring data of a virtual district heating network with 146 consumers and an existing district heating network with 66 consumers. These evaluations are based on comparison of the simulation results of the aggregated network with fewer consumers than the original network in terms of accuracy, information loss and computational time. The results show that the CPU time in the simulation of this network can speed up approximately more than 95% by aggregating the network down to 3 consumers without significant information loss. We will highlight the current potentials and limitations of both aggregation methods in terms of integrating them in the planning, modelling and operation of DHC systems, including the 4th generation district heating systems.

A combined NN and dynamic gain-based approach to further stabilize nonlinear time-delay systems

This paper focuses on the state-feedback control problem for a class of high-order nonlinear systems with unknown time delay and control coefficients. Based on a novel dynamic gain-based backstepping technique and radial basis function neural network (RBF NN) approximation approach, the restrictions on high-order and nonlinearities are removed or further relaxed. Under these weaker conditions, a smooth state-feedback controller is skillfully constructed with only one adaptive parameter. In addition, the knowledge of time delay, NN nodes and weights is not necessary to be known a priori. It is proven that the designed controller can render the closed-loop system be semi-globally uniformly ultimately bounded. Finally, both practical and numerical examples are shown to demonstrate the effectiveness of the proposed scheme.

Learning word dependencies in text by means of a deep recurrent belief network

We propose a deep recurrent belief network with distributed time delays for learning multivariate Gaussians. Learning long time delays in deep belief networks is difficult due to the problem of vanishing or exploding gradients with increase in delay. To mitigate this problem and improve the transparency of learning time-delays, we introduce the use of Gaussian networks with time-delays to initialize the weights of each hidden neuron. From our knowledge of time delays, it is possible to learn the long delays from short delays in a hierarchical manner. In contrast to previous works, here dynamic Gaussian Bayesian networks over training samples are evolved using Markov Chain Monte Carlo to determine the initial weights of each hidden layer of neurons. In this way, the time-delayed network motifs of increasing Markov order across layers can be modeled hierarchically using a deep model. To validate the proposed Variable-order Belief Network (VBN) framework, it is applied for modeling word dependencies in text. To explore the generality of VBN, it is further considered for a real-world scenario where the dynamic movements of basketball players are modeled. Experimental results obtained showed that the proposed VBN could achieve over 30% improvement in accuracy on real-world scenarios compared to the state-of-the-art baselines.

Minimum Variance Benchmark for Performance Assessment of Decentralized Controllers

The available minimum variance (MV) benchmarks do not take the controller structure into account and thus can lead to incorrect conclusions regarding the performance of decentralized controllers. In this paper, we present a method for computing a lower bound on the least output variance achievable using decentralized controllers, where the nonconvexity of the optimization problem is handled using sums of squares programming. Though tight, the computation of this lower bound requires that the process model be known. As a more practical approach, we derive an MV benchmark for performance assessment of decentralized controllers on a loop by loop basis. The MV benchmark for loop by loop analysis can be estimated from the closed loop operating data with the knowledge of time delays between all the inputs and outputs. The usefulness of the proposed benchmarks is demonstrated using simulation examples.