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

Architecture of a network-in-the-Loop environment for characterizing AC power system behavior

This paper describes the method by which a large hardware-in-the-loop environment has been realized for three-phase ac power systems. The environment allows an entire laboratory power-network topology (generators, loads, controls, protection devices, and switches) to be placed in the loop of a large power-network simulation. The system is realized by using a realtime power-network simulator, which interacts with the hardware via the indirect control of a large synchronous generator and by measuring currents flowing from its terminals. These measured currents are injected into the simulation via current sources to close the loop. This paper describes the system architecture and, most importantly, the calibration methodologies which have been developed to overcome measurement and loop latencies. In particular, a new "phase advance" calibration removes the requirement to add unwanted components into the simulated network to compensate for loop delay. The results of early commissioning experiments are demonstrated. The present system performance limits under transient conditions (approximately 0.25 Hz/s and 30 V/s to contain peak phase-and voltage-tracking errors within 5. and 1%) are defined mainly by the controllability of the synchronous generator

European White Book on Real-Time Power Hardware in the Loop Testing : DERlab Report No. R- 005.0

The European White Book on Real-Time-Powerhardware-in-the-Loop testing is intended to serve as a reference document on the future of testing of electrical power equipment, with specifi c focus on the emerging hardware-in-the-loop activities and application thereof within testing facilities and procedures. It will provide an outlook of how this powerful tool can be utilised to support the development, testing and validation of specifi cally DER equipment. It aims to report on international experience gained thus far and provides case studies on developments and specifi c technical issues, such as the hardware/software interface. This white book compliments the already existing series of DERlab European white books, covering topics such as grid-inverters and grid-connected storag

Real-Time Implementation of Intelligent Modeling and Control Techniques on a PLC Platform

Programmable logic controllers (PLCs) have been used for many decades for standard control in industrial and factory environments. Over the years, PLCs have become computational efficient and powerful, and a robust platform with applications beyond the standard control and factory automation. Due to the new advanced PLC\u27s features and computational power, they are ideal platforms for exploring advanced modeling and control methods, including computational intelligence based techniques such as neural networks, particle swarm optimization (PSO) and many others. Some of these techniques require fast floating-point calculations that are now possible in real-time on the PLC. This paper focuses on the Allen-Bradley ControlLogix brand of PLCs, due to their high performance and extensive use in industry. The design and implementation of a neurocontroller consisting of two neural networks, one for modeling and the other for control, and the training of these neural networks with particle swarm optimization is presented in this paper on a single PLC. The neurocontroller in this study is a power system stabilizer (PSS) that is used for power system oscillation damping. The PLC is interfaced to a power system simulated on the real time digital simulator. Real time results are presented showing that the PLC is a suitable hardware platform for implementing advanced modeling and control techniques for industrial applications

Implementation of Adaptive Critic-Based Neurocontrollers for Turbogenerators in a Multimachine Power System

This paper presents the design and practical hardware implementation of optimal neurocontrollers that replace the conventional automatic voltage regulator (AVR) and the turbine governor of turbogenerators on multimachine power systems. The neurocontroller design uses a powerful technique of the adaptive critic design (ACD) family called dual heuristic programming (DHP). The DHP neurocontroller\u27s training and testing are implemented on the Innovative Integration M67 card consisting of the TMS320C6701 processor. The measured results show that the DHP neurocontrollers are robust and their performance does not degrade unlike the conventional controllers even when a power system stabilizer (PSS) is included, for changes in system operating conditions and configurations. This paper also shows that it is possible to design and implement optimal neurocontrollers for multiple turbogenerators in real time, without having to do continually online training of the neural networks, thus avoiding risks of instability

Impact Assessment of Hypothesized Cyberattacks on Interconnected Bulk Power Systems

The first-ever Ukraine cyberattack on power grid has proven its devastation by hacking into their critical cyber assets. With administrative privileges accessing substation networks/local control centers, one intelligent way of coordinated cyberattacks is to execute a series of disruptive switching executions on multiple substations using compromised supervisory control and data acquisition (SCADA) systems. These actions can cause significant impacts to an interconnected power grid. Unlike the previous power blackouts, such high-impact initiating events can aggravate operating conditions, initiating instability that may lead to system-wide cascading failure. A systemic evaluation of "nightmare" scenarios is highly desirable for asset owners to manage and prioritize the maintenance and investment in protecting their cyberinfrastructure. This survey paper is a conceptual expansion of real-time monitoring, anomaly detection, impact analyses, and mitigation (RAIM) framework that emphasizes on the resulting impacts, both on steady-state and dynamic aspects of power system stability. Hypothetically, we associate the combinatorial analyses of steady state on substations/components outages and dynamics of the sequential switching orders as part of the permutation. The expanded framework includes (1) critical/noncritical combination verification, (2) cascade confirmation, and (3) combination re-evaluation. This paper ends with a discussion of the open issues for metrics and future design pertaining the impact quantification of cyber-related contingencies

Transient stability enhancement of wind farms connected to a multi-machine power system by using an adaptive ANN-controlled SMES

This paper presents a novel adaptive artificial neural network (ANN)-controlled superconducting magnetic energy storage (SMES) system to enhance the transient stability of wind farms connected to a multi-machine power system during network disturbances. The control strategy of SMES depends mainly on a sinusoidal pulse width modulation (PWM) voltage source converter (VSC) and an adaptive ANN-controlled DC-DC converter using insulated gate bipolar transistors (IGBTs). The effectiveness of the proposed adaptive ANN-controlled SMES is then compared with that of proportional-integral (PI)-controlled SMES optimized by response surface methodology and genetic algorithm (RSM-GA) considering both of symmetrical and unsymmetrical faults. For realistic responses, real wind speed data and two-mass drive train model of wind turbine generator system is considered in the analyses. The validity of the proposed system is verified by the simulation results which are performed using the laboratory standard dynamic power system simulator PSCAD/EMTDC. Notably, the proposed adaptive ANN-controlled SMES enhances the transient stability of wind farms connected to a multi-machine power system