Discrete control algorithms for a tubular ammonia reactor

The specific objective of this paper is to develop direct digital control strategies for an ammonia reactor using quadratic regulator theory and compare the performance of the resultant control system with that under conventional PID regulators. The controller design studies are based on a ninth order state-space model obtained from the exact nonlinear distributed model using linearization and lumping approximations. The evaluation of these controllers with reference to their disturbance rejection capabilities and transient response characteristics, is carried out using hybrid computer simulation.

Reinforcement learning controllers for automatic generation control in power systems having re-heat units with GRC and dead-band

A new automatic generation controller (AGC) design approach, adopting reinforcement learning (RL) techniques, was recently pro- posed [1]. In this paper we demonstrate the design and performance of controllers based on this RL approach for automatic generation control of systems consisting of units having complex dynamics—the reheat type of thermal units. For such systems, we also assess the capabilities of RL approach in handling realistic system features such as network changes, parameter variations, generation rate constraint (GRC), and governor deadband.

Fuzzy Expert Approach with Curtailed Controllers for Improved Reactive Power Dispatch

This paper addresses the problem of curtailing the number of control actions using fuzzy expert approach for voltage/reactive power dispatch. It presents an approach using fuzzy set theory for reactive power control with the purpose of improving the voltage profile of a power system. To minimize the voltage deviations from pre-desired values of all the load buses, using the sensitivities with respect to reactive power control variables form the basis of the proposed Fuzzy Logic Control (FLC). Control variables considered are switchable VAR compensators, On Load Tap Changing (OLTC) transformers and generator excitations. Voltage deviations and controlling variables are translated into fuzzy set notations to formulate the relation between voltage deviations and controlling ability of controlling devices. The developed fuzzy system is tested on a few simulated practical Indian power systems and modified IEEE-30 bus system. The performance of the fuzzy system is compared with conventional optimization technique and results obtained are encouraging. Results obtained for a modified IEEE-30 bus test system and a 205-node equivalent EHV system a part of Indian southern grid are presented for illustration purposes. The proposed fuzzy-expert technique is found suitable for on-line applications in energy control centre as the solution is obtained fast with significant speedups with few number of controllers.

Collective behavior with heterogeneous controllers

In this paper, we study the collective motion of individually controlled planar particles when they are coupled through heterogeneous controller gains. Two types of collective formations, synchronization and balancing, are described and analyzed under the influence of these heterogeneous controller gains. These formations are characterized by the motion of the centroid of the group of particles. In synchronized formation, the particles and their centroid move in a common direction, while in balanced formation the movement of particles possess a fixed location of the centroid. We show that, by selecting suitable controller gains, these formations can be controlled significantly to obtain not only a desired direction of motion but also a desired location of the centroid. We present the results for N-particles in synchronized formation, while in balanced formation our analysis is confined to two and three particles.

基于人工神经网络的水洞试验装置的PID自动控制系统

在水洞试验中 ,通过水泵及收缩段的形状控制水洞的运行过程 ,以保证水洞的稳定运行 ,并保证所进行的各种试验的质量。PID调节是最常见的一种。文章给出了一种基于人工神经网络实现自学习PID控制方法。利用该方法可以较好地实现水洞的控制