A reconfigurable distributed multiagent system optimized for scalability

This thesis proposes a novel solution for optimizing the size and communication overhead of a distributed multiagent system without compromising the performance. The proposed approach addresses the challenges of scalability especially when the multiagent system is large. A modified spectral clustering technique is used to partition a large network into logically related clusters. Agents are assigned to monitor dedicated clusters rather than monitor each device or node. The proposed scalable multiagent system is implemented using JADE (Java Agent Development Environment) for a large power system. The performance of the proposed topology-independent decentralized multiagent system and the scalable multiagent system is compared by comprehensively simulating different fault scenarios. The time taken for reconfiguration, the overall computational complexity, and the communication overhead incurred are computed. The results of these simulations show that the proposed scalable multiagent system uses fewer agents efficiently, makes faster decisions to reconfigure when a fault occurs, and incurs significantly less communication overhead. The proposed scalable multiagent system has been coupled with a scalable reconfiguration algorithm for an electric power system attempting to minimize the number of switch combination explored for reconfiguration. The reconfiguration algorithm reconfigures a power system while maintaining bus voltages within limits specified by constraints

A Multiagent Q-learning-based Restoration Algorithm for Resilient Distribution System Operation

Natural disasters, human errors, and technical issues have caused disastrous blackouts to power systems and resulted in enormous economic losses. Moreover, distributed energy resources have been integrated into distribution systems, which bring extra uncertainty and challenges to system restoration. Therefore, the restoration of power distribution systems requires more efficient and effective methods to provide resilient operation. In the literature, using Q-learning and multiagent system (MAS) to restore power systems has the limitation in real system application, without considering power system operation constraints. In order to adapt to system condition changes quickly, a restoration algorithm using Q-learning and MAS, together with the combination method and battery algorithm is proposed in this study. The developed algorithm considers voltage and current constraints while finding system switching configuration to maximize the load pick-up after faults happen to the given system. The algorithm consists of three parts. First, it finds switching configurations using Q-learning. Second, the combination algorithm works as a back-up plan in case of the solution from Q-learning violates system constraints. Third, the battery algorithm is applied to determine the charging or discharging schedule of battery systems. The obtained switching configuration provides restoration solutions without violating system constraints. Furthermore, the algorithm can adjust switching configurations after the restoration. For example, when renewable output changes, the algorithm provides an adjusted solution to avoid violating system constraints. The proposed algorithm has been tested in the modified IEEE 9-bus system using the real-time digital simulator. Simulation results demonstrate that the algorithm offers an efficient and effective restoration strategy for resilient distribution system operation

Toward Fault Adaptive Power Systems in Electric Ships

Shipboard Power Systems (SPS) play a significant role in next-generation Navy fleets. With the increasing power demand from propulsion loads, ship service loads, weaponry systems and mission systems, a stable and reliable SPS is critical to support different aspects of ship operation. It also becomes the technology-enabler to improve ship economy, efficiency, reliability, and survivability. Moreover, it is important to improve the reliability and robustness of the SPS while working under different operating conditions to ensure safe and satisfactory operation of the system. This dissertation aims to introduce novel and effective approaches to respond to different types of possible faults in the SPS. According to the type and duration, the possible faults in the Medium Voltage DC (MVDC) SPS have been divided into two main categories: transient and permanent faults. First, in order to manage permanent faults in MVDC SPS, a novel real-time reconfiguration strategy has been proposed. Onboard postault reconfiguration aims to ensure the maximum power/service delivery to the system loads following a fault. This study aims to implement an intelligent real-time reconfiguration algorithm in the RTDS platform through an optimization technique implemented inside the Real-Time Digital Simulator (RTDS). The simulation results demonstrate the effectiveness of the proposed real-time approach to reconfigure the system under different fault situations. Second, a novel approach to mitigate the effect of the unsymmetrical transient AC faults in the MVDC SPS has been proposed. In this dissertation, the application of combined Static Synchronous Compensator (STATCOM)-Super Conducting Fault Current Limiter (SFCL) to improve the stability of the MVDC SPS during transient faults has been investigated. A Fluid Genetic Algorithm (FGA) optimization algorithm is introduced to design the STATCOM\u27s controller. Moreover, a multi-objective optimization problem has been formulated to find the optimal size of SFCL\u27s impedance. In the proposed scheme, STATCOM can assist the SFCL to keep the vital load terminal voltage close to the normal state in an economic sense. The proposed technique provides an acceptable post-disturbance and postault performance to recover the system to its normal situation over the other alternatives

A Model-Based Holistic Power Management Framework: A Study on Shipboard Power Systems for Navy Applications

The recent development of Integrated Power Systems (IPS) for shipboard application has opened the horizon to introduce new technologies that address the increasing power demand along with the associated performance specifications. Similarly, the Shipboard Power System (SPS) features system components with multiple dynamic characteristics and require stringent regulations, leveraging a challenge for an efficient system level management. The shipboard power management needs to support the survivability, reliability, autonomy, and economy as the key features for design consideration. To address these multiple issues for an increasing system load and to embrace future technologies, an autonomic power management framework is required to maintain the system level objectives. To address the lack of the efficient management scheme, a generic model-based holistic power management framework is developed for naval SPS applications. The relationship between the system parameters are introduced in the form of models to be used by the model-based predictive controller for achieving the various power management goals. An intelligent diagnostic support system is developed to support the decision making capabilities of the main framework. Naïve Bayes’ theorem is used to classify the status of SPS to help dispatch the appropriate controls. A voltage control module is developed and implemented on a real-time test bed to verify the computation time. Variants of the limited look-ahead controls (LLC) are used throughout the dissertation to support the management framework design. Additionally, the ARIMA prediction is embedded in the approach to forecast the environmental variables in the system design. The developed generic framework binds the multiple functionalities in the form of overall system modules. Finally, the dissertation develops the distributed controller using the Interaction Balance Principle to solve the interconnected subsystem optimization problem. The LLC approach is used at the local level, and the conjugate gradient method coordinates all the lower level controllers to achieve the overall optimal solution. This novel approach provides better computing performance, more flexibility in design, and improved fault handling. The case-study demonstrates the applicability of the method and compares with the centralized approach. In addition, several measures to characterize the performance of the distributed controls approach are studied

Immune System Based Control and Intelligent Agent Design for Power System Applications

The National Academy of Engineering has selected the US Electric Power Grid as the supreme engineering achievement of the 20th century. Yet, this same grid is struggling to keep up with the increasing demand for electricity, its quality and cost. A growing recognition of the need to modernize the grid to meet future challenges has found articulation in the vision of a Smart Grid in using new control strategies that are intelligent, distributed, and adaptive. The objective of this work is to develop smart control systems inspired from the biological Human Immune System to better manage the power grid at the both generation and distribution levels. The work is divided into three main sections. In the first section, we addressed the problem of Automatic Generation Control design. The Clonal Selection theory is successfully applied as an optimization technique to obtain decentralized control gains that minimize a performance index based on Area Control Errors. Then the Immune Network theory is used to design adaptive controllers in order to diminish the excess maneuvering of the units and help the control areas comply with the North American Electric Reliability Corporation\u27s standards set to insure good quality of service and equitable mutual assistance by the interconnected energy balancing areas. The second section of this work addresses the design and deployment of Multi Agent Systems on both terrestrial and shipboard power systems self-healing using a novel approach based on the Immune Multi-Agent System (IMAS). The Immune System is viewed as a highly organized and distributed Multi-Cell System that strives to heal the body by working together and communicating to get rid of the pathogens. In this work both simulation and hardware design and deployment of the MAS are addressed. The third section of this work consists in developing a small scale smart circuit by modifying and upgrading the existing Analog Power Simulator to demonstrate the effectiveness of the developed technologies. We showed how to develop smart Agents hardware along with a wireless communication platform and the electronic switches. After putting together the different designed pieces, the resulting Multi Agent System is integrated into the Power Simulator Hardware. The multi Agent System developed is tested for fault isolation, reconfiguration, and restoration problems by simulating a permanent three phase fault on one of the feeder lines. The experimental results show that the Multi Agent System hardware developed performed effectively and in a timely manner which confirms that this technology is very promising and a very good candidate for Smart Grid control applications

Control and Management of Hybrid Renewable Energy Systems: Review and Comparison of Methods

Hybrid renewable energy systems (HRES) have been introduced to overcome intermittent nature of single-source renewable energy generation. In order to utilize HRES optimally, two issues must be considered: optimal sizing and optimal operation. The first issue has been considered vastly in several articles but the second one needs more attention and work. The performance of hybrid renewable energy systems highly depends on how efficient the control of energy production is. In this paper, paradigms and common methods available for control and management of energy in HRES are reviewed and compared with each other. At the end, a number of challenges and future research in relation to HRES are addressed

A Recursive Watermark Method for Hard Real-Time Industrial Control System Cyber-Resilience Enhancement

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.International audienceCybersecurity is of vital importance to industrial control systems (ICSs), such as ship automation, manufacturing, building, and energy automation systems. Many control applications require hard real-time channels, where the delay and jitter are in the levels of milliseconds or less. To the best of our knowledge, no encryption algorithm is fast enough for hard real-time channels of existing industrial fieldbuses and, therefore, made mission-critical applications vulnerable to cyberattacks, e.g., delay and data injection attacks. In this article, we propose a novel recursive watermark (RWM) algorithm for hard real-time control system data integrity validation. Using a watermark key, a transmitter applies watermark noise to hard real-time signals and sends through the unencrypted hard real-time channel. The same key is transferred to the receiver by the encrypted nonreal-time channel. With the same key, the receiver can detect if the data have been modified by the attackers and take action to prevent catastrophic damages. We provide analysis and methods to design proper watermark keys to ensure reliable attack detection. We use a ship propulsion control system for the simulation-based case study, where our algorithm smoothly shuts down the system after attacks. We also evaluated the algorithm speed on a Siemens S7-1500 programmable logic controller (PLC). This hardware experiment demonstrated that the RWM algorithm takes about 2.8 μs to add or validate the watermark noise on one sample data point. As a comparison, common cryptic hashing algorithms can hardly process a small data set under 100 ms. The proposed RWM is about 32 to 1375 times faster than the standard approaches

Review on Control of DC Microgrids and Multiple Microgrid Clusters

This paper performs an extensive review on control schemes and architectures applied to dc microgrids (MGs). It covers multilayer hierarchical control schemes, coordinated control strategies, plug-and-play operations, stability and active damping aspects, as well as nonlinear control algorithms. Islanding detection, protection, and MG clusters control are also briefly summarized. All the mentioned issues are discussed with the goal of providing control design guidelines for dc MGs. The future research challenges, from the authors' point of view, are also provided in the final concluding part

Real-time Power Management of Hybrid Power Systems in All Electric Ship Applications.

Motivated by the need for achieving flexible shipboard arrangement and meeting future on-board power demand, the concept of all-electric ships (AES) has been pursued. The integrated power systems enable this initiative by providing a common electrical platform for the propulsion and ship-service loads and are a classic example of hybrid power systems (HPS). In order to leverage the complementary dynamic characteristics of the diverse sources, effective power management (PM) is essential to coordinate the sources and energy storage to achieve efficient power generation and fast load following. Although extensive research has been done on the PM of hybrid land vehicles for commercial applications, this problem for shipboard military applications remains largely unaddressed, leading to its exclusive focus in this dissertation. While HPS brings in many opportunities for power management, there are many associated challenges for systems used in military applications since both performance as well as survivability criteria have to be satisfied. While the on-demand goal for the power management problem makes real-time control a key requirement, leveraging the look-ahead opportunities for the shipboard missions makes it difficult to attain this goal. Furthermore, the nonlinearity and the complexity of hybrid power systems, make the optimal control of HPS challenging. In this dissertation, we address real-time power management for the AES and general hybrid power systems targeting military applications. The central theme of this work is the development of power management schemes with real-time computational efficiency by exploring HPS dynamic properties, for improved performance (namely fuel economy and fast load following) during normal mode conditions as well as increased survivability during component failure. A reduced order dynamic HPS model and a scaled test bed is developed as a numerical tool for controller design and validation. The power management (PM) schemes for both normal as well as failure mode conditions are proposed and implemented on a real-time simulator which demonstrated the real-time performance of the proposed method. While the normal mode PM leverages the complementary dynamic characteristics of the HPS for real-time look-ahead control and performance, the failure mode PM uses a reference governor approach for real-time constraint enforcement.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/77863/1/gseenuma_1.pd