Optimal excitation controllers, and location and sizing of energy storage for all-electric ship power system

The Navy\u27s future all-electric ship power system is based on the integrated power system (IPS) architecture consisting of power generation, propulsion systems, hydrodynamics, and DC zonal electric distribution system (DC-ZEDS). To improve the power quality, optimal excitation systems, and optimal location and sizing of energy storage modules (ESMs) are studied. In this dissertation, clonal selection algorithm (CSA) based controller design is firstly introduced. CSA based controller design shows better exploitation ability with relatively long search time when compared to a particle swarm optimization (PSO) based design. Furthermore, \u27optimal\u27 small population PSO (SPPSO) based excitation controller is introduced. Parameter sensitivity analysis shows that the parameters of SPPSO for regeneration can be fined tuned to achieve fast optimal controller design, and thus exploiting SPPSO features for problem of particles get trapped in local minima and long search time. Furthermore, artificial immune system based concepts are used to develop adaptive and coordinated excitation controllers for generators on ship IPS. The computational approaches for excitation controller designs have been implemented on digital signal processors interfaced to an actual laboratory synchronous machine, and to multimachine electric ship power systems simulated on a real-time digital simulator. Finally, an approach to evaluate ESM location and sizing is proposed using three metrics: quality of service, survivability and cost. Multiple objective particle swarm optimization (MOPSO) is used to optimize these metrics and provide Pareto fronts for optimal ESM location and sizing --Abstract, page iv

Wide Area Signals Based Damping Controllers for Multimachine Power Systems

Nowadays, electric power systems are stressed and pushed toward their stability margins due to increasing load demand and growing penetration levels of renewable energy sources such as wind and solar power. Due to insufficient damping in power systems, oscillations are likely to arise during transient and dynamic conditions. To avoid undesirable power system states such as tripping of transmission lines, generation sources, and loads, eventually leading to cascaded outages and blackouts, intelligent coordinated control of a power system and its elements, from a global and local perspective, is needed. The research performed in this dissertation is focused on intelligent analysis and coordinated control of a power system to damp oscillations and improve its stability. Wide area signals based coordinated control of power systems with and without a wind farm and energy storage systems is investigated. A data-driven method for power system identification is developed to obtain system matrices that can aid in the design of local and wide area signals based power system stabilizers. Modal analysis is performed to characterize oscillation modes using data-driven models. Data-driven models are used to identify the most appropriate wide-area signals to utilize as inputs to damping controller(s) and generator(s) to receive supplementary control. Virtual Generators (VGs) are developed using the phenomena of generator coherency to effectively and efficiently control power system oscillations. VG based Power System Stabilizers (VG-PSSs) are proposed for optimal damping of power system oscillations. Herein, speed deviation of VGs is used to generate a supplementary coordinated control signal for an identified generator(s) of maximum controllability. The parameters of a VG-PSS(s) are heuristically tuned to provide maximum system damping. To overcome fallouts and switching in coherent generator groups during transients, an adaptive inter-area oscillation damping controller is developed using the concept of artificial immune systems - innate and adaptive immunity. With increasing levels of electric vehicles (EVs) on the road, the potential of SmartParks (a large number of EVs in parking lots) for improving power system stability is investigated. Intelligent multi-functional control of SmartParks using fuzzy logic based controllers are investigated for damping power system oscillations, regulating transmission line power flows and bus voltages. In summary, a number of approaches and suggestions for improving modern power system stability have been presented in this dissertation

Hardware Implementation of an AIS-Based Optimal Excitation Controller for an Electric Ship

The operation of high energy loads on Navy\u27s future electric ships, such as directed energy weapons, will cause disturbances in the main bus voltage and impact the operation of the rest of the power system when the pulsed loads are directly powered from the main dc bus. This paper describes an online design and laboratory hardware implementation of an optimal excitation controller using an artificial immune system (AIS) based algorithm. The AIS algorithm, a clonal selection algorithm (CSA), is used to minimize the effects of pulsed loads by improved excitation control and thus, reduce the requirement on energy storage device capacity. The CSA is implemented on the MSK2812 DSP hardware platform. A comparison of CSA and the particle swarm optimization (PSO) algorithm is presented. Hardware measurement results show that the CSA optimized excitation controller provides effective control of a generator\u27s terminal voltage during pulsed loads, restoring and stabilizing it quickly

Improved gravitational search algorithm for proportional integral derivative controller tuning in process control system

Proportional-Integral-Derivative (PID) controller is one of the most used controllers in the industry due to the reliability and simplicity of its structure. However, despite its simple structure controller, the tuning process of PID controller for nonlinear, high-order and complex plant is difficult and faces lots of challenges. Conventional method such as Ziegler-Nichols are still being used for PID tuning process despite its lack of tuning accuracy. Nowadays researchers around the world shift their attention from conventional method to optimisation-based methods. For the last five years, optimisation techniques become one of the most popular methods used for tuning process of PID controller. Optimisation techniques such as Genetic Algorithm (GA), Particle Swarm Optimisation (PSO) as well as Gravitational Search Algorithm (GSA) are widely used for the PID controller application. Despite the effectiveness of GSA for PID controller tuning process compared to the GA and PSO technique, there is still a room for improvement of GSA performance for PID controller tuning process. This research represents the additional characters in GSA to enhance the PID controller parameter tuning performance which are Linear Weight Summation (LWS) and alpha parameter range tuning. Performance of optimisation-based PID controllers are measured based on the transient response performance specification (i.e. rise time, settling time, and percentage overshoot). By implementing these two approaches, results show that Improved Gravitational Search Algorithm (IGSA) based PID controller produced 20% to 30% faster rise and settling time and 25% to 35% smaller percentage overshoot compared to GA-PID and PSO-PID. For real implementation analysis, IGSA based PID controller also produced faster settling time and lower percentage overshoot than other optimisation-based PID controller. A good controller viewed as a controller that produced a stable dynamic system. Therefore, by producing a good transient response, IGSA based PID controller is able to provide a stable dynamic system performance compared to other controllers

The 1st International Conference on Computational Engineering and Intelligent Systems

Computational engineering, artificial intelligence and smart systems constitute a hot multidisciplinary topic contrasting computer science, engineering and applied mathematics that created a variety of fascinating intelligent systems. Computational engineering encloses fundamental engineering and science blended with the advanced knowledge of mathematics, algorithms and computer languages. It is concerned with the modeling and simulation of complex systems and data processing methods. Computing and artificial intelligence lead to smart systems that are advanced machines designed to fulfill certain specifications. This proceedings book is a collection of papers presented at the first International Conference on Computational Engineering and Intelligent Systems (ICCEIS2021), held online in the period December 10-12, 2021. The collection offers a wide scope of engineering topics, including smart grids, intelligent control, artificial intelligence, optimization, microelectronics and telecommunication systems. The contributions included in this book are of high quality, present details concerning the topics in a succinct way, and can be used as excellent reference and support for readers regarding the field of computational engineering, artificial intelligence and smart system

Design Low Order Robust Controller for the Generator’s Rotor Angle Stabilization PSS System

The electrical system's problem stabilizes the electrical system with three primary parameters: rotor angle stability, frequency stability, and voltage stability. This paper focuses on the problem of designing a low-order stable optimal controller for the generator rotor angle (load angle) stabilization system with minor disturbances. These minor disturbances are caused by lack of damping torque, change in load, or change in a generator during operation. Using the RH∞optimal robust design method for the Power System Stabilizer (PSS) to stabilize the generator’s load angle will help the PSS system work sustainably under disturbance. However, this technique's disadvantage is that the controller often has a high order, causing many difficulties in practical application. To overcome this disadvantage, we propose to reduce the order of the higher-order optimal robust controller. There are two solutions to reduce order for high-order optimal robust controller: optimal order reduction according to the given controller structure and order reduction according to model order reduction algorithms. This study selects the order reduction of the controller according to the model order reduction algorithms. In order to choose the most suitable low-order optimal robust controller that can replace the high-order optimal robust controller, we have compared and evaluated the order-reducing controllers according to many model order reduction algorithms. Using robust low-order controllers to control the generator’s rotor angle completely meets the stabilization requirements. The research results of the paper show the correctness of the controller order reduction solution according to the model order reduction algorithms and open the possibility of application in practice. Doi: 10.28991/esj-2021-01299 Full Text: PD

Evolutionary Algorithms in Engineering Design Optimization

Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. Their main advantages are the following: they do not require any requisite to the objective/fitness evaluation function (continuity, derivability, convexity, etc.); they are not limited by the appearance of discrete and/or mixed variables or by the requirement of uncertainty quantification in the search. Moreover, they can deal with more than one objective function simultaneously through the use of evolutionary multi-objective optimization algorithms. This set of advantages, and the continuously increased computing capability of modern computers, has enhanced their application in research and industry. From the application point of view, in this Special Issue, all engineering fields are welcomed, such as aerospace and aeronautical, biomedical, civil, chemical and materials science, electronic and telecommunications, energy and electrical, manufacturing, logistics and transportation, mechanical, naval architecture, reliability, robotics, structural, etc. Within the EA field, the integration of innovative and improvement aspects in the algorithms for solving real world engineering design problems, in the abovementioned application fields, are welcomed and encouraged, such as the following: parallel EAs, surrogate modelling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc