Model predictive load–frequency control taking into account imbalance uncertainty

© 2015 Elsevier Ltd.Nonlinear model predictive control (NMPC) is investigated for load frequency control (LFC) of an interconnected power system which is exposed to increasing wind power penetration. The robustified NMPC (RNMPC) proposed here uses knowledge of the estimated worst-case deviation in wind-power production to make the NMPC more robust. The NMPC is based on a simplified system model that is updated using state- and parameter estimation by Kalman filters, and it takes into account limitations on among others tie-line power flow. Tests on a proxy of the Nordic power system show that the RNMPC is able to fulfill system constraints under worst-case deviations in wind-power production, where the nominal NMPC is not

Model-Predictive Control for Alleviating Transmission Overloads and Voltage Collapse in Large-Scale Electric Power Systems

Emergency control in electric power systems requires rapid identification and implementation of corrective actions. Typically, system operators have performed this service while relying on rules-of-thumb and predetermined control sequences with limited decision support tools. Automatic control schemes offer the potential to improve this process by quickly analyzing large, complex problems to identify the most effective actions. Model-predictive control (MPC) is one such scheme which has a strong record of success in the process industry and has begun receiving attention in power systems applications. Incorporating flexibility into the MPC model using energy storage and temperature-based transmission line limits has shown promising results for relieving transmission overloads on small networks with linear active power models. Separately, MPC has demonstrated its capabilities in correcting transformer-driven voltage collapse behaviors. However, a comprehensive solution combines both aspects into a single controller formulation with knowledge of active and reactive power and voltage magnitude and angle. Additionally, most power system networks are large and result in computationally challenging problem formulations. This work considers these practical limitations and suggests techniques to enable an MPC process capable of operating reliably in the real-world. A new linear controller model is proposed which considers voltage magnitude and angle and both active and reactive power. The new model provides greater accuracy when predicting system behavior and better identifies the actual control needs of the system. The problem size is reduced by limiting the model to only those devices which are significantly affected by the emergency conditions. The new approach is shown to identify controls more rapidly and better suppresses undesirable thermal behavior on overloaded transmission lines while avoiding potential voltage collapse situations.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137120/1/jandrewm_1.pd

Algorithm selection for power flow management

PhD ThesisAlgorithms are essential for solving many important problems, including in power systems control, where they can allow the connection of new demand and generation whilst deferring or avoiding the need for network reinforcement. However, in many problem domains no algorithm always delivers the best performance for all problems, so better performance can be achieved by using algorithm selection to select the best algorithms for each problem. This work applies algorithm selection to power systems control, with power flow management using generator curtailment examined as a representative power systems control task. The first half of this work focuses on whether potential performance benefits are available if algorithms are selected optimally for each network state. Five power flow management algorithms are implemented, which use diverse approaches such as optimal power flow, constraint satisfaction, power flow sensitivity factors, and linear programming. Four case study power systems – an 11 kV radial distribution system, a 33 kV meshed distribution system, the IEEE 14-bus system, and the IEEE 57-bus system – are used to test the algorithms over a extensive range of network states. None of the algorithms give the most effective performance for every state, in terms of minimising either the number or energy of overloads, whilst minimising curtailment. By optimally selecting algorithms for each state there are potential performance benefits for three of the four case study systems In the second half of this work, algorithm selection systems (selectors) are created in order to exploit and deliver the observed potential performance benefits of per-state algorithm selection. Existing techniques for creating algorithm selectors are adapted and extended for the power flow management application, which includes the development of a training method that allows selectors to consider two objectives simultaneously. The selectors created take measurements of network state as input and use machine learning models to make algorithm selection decisions. The models either directly predict which algorithm is likely to be the most effective, or predict the performance of each algorithm, with the algorithm with the most effective predicted performance then being selected. Both of these approaches are shown to be effective in creating algorithm selectors for power flow management that deliver statistically significant performance benefits. In some cases, the selectors are able to match the optimum performance that could be achieved by selecting between the algorithms.WSP | Parsons Brinckerhoff

Emerging technologies and future trends in substation automation systems for the protection, monitoring and control of electrical substations

Tese de Mestrado Integrado. Engenharia Electrotécnica e de Computadores (Automação). Faculdade de Engenharia. Universidade do Porto. 201

Optimization and Model-predictive Control for Overload Mitigation in Resilient Power Systems.

The National Academy of Engineering named the electric power grid the greatest engineering achievement of the 20th century. However, as recent large-scale power grid failures illustrate, the (electro-mechanical) electric grid is being operated closer and closer to its limits. Specifically, the electric grid of the 20th century is aging and congested. Due to the protracted and cost-intensive nature of upgrading energy infrastructures, major research initiatives are now underway to improve the utility of the existing infrastructure. One important topic is contingency management. Accordingly, this dissertation comprises of practical, yet rigorously justified, feedback control algorithms that are suitable for power system contingency management. The main goals of the algorithms are to prevent or mitigate overloads on network elements (e.g. lines and transformers). In this dissertation, a coupling of energy infrastructures is examined as a method for improving system reliability and a simple cascade mitigation approach highlights the role of model-predictive control and energy storage in improving system response to severe disturbances (e.g. line outages). The ideas of balancing economic and safety criteria are developed and implemented with a receding-horizon model-predictive controller (RHMPC) for electric transmission systems with energy storage and renewables. The novel RHMPC scheme employs a lossy "DC" power flow model and is proven to alleviate conductor temperature overloads and returns the system to an economically optimal state. Finally, an incentive-based distributed predictive-control algorithm is developed to prevent overloads in the distribution network caused by overnight charging of plug-in electric vehicles. In addition, Matlab-based simulations are included to illustrate the performance and behavior of all proposed overload mitigation schemes. The automatic schemes presented in this dissertation are, essentially, "closing the loop'' in contingency management, and will help bring the electric power grid into the 21st century.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/100049/1/malmassa_1.pd

Adaptive Three-Stage Controlled Islanding to Prevent Imminent Wide-area Blackouts

Power blackouts are a recurring problem worldwide, and research in this area continues to focus on developing improved methods for their prediction and prevention. Controlled islanding has been proposed as a last resort action to save the network before imminent blackouts when the usual means fail in an unexpected manner. Successful controlled islanding has to deal with three important issues that are involved in the implementation of islanding: when to island, where to island and what to do after islanding is implemented in each island. This thesis presents a framework that combines all three issues to achieve successful islanding based on wide area measurement systems (WAMS). In addition, this thesis focuses on the question of when to island. This question is critical to the success of the three-stage controlled islanding scheme because the possible issues of false dismissal and false alarm have to be handled. In false dismissal, islanding is triggered too late. However, the potentially unstable system is still allowed to operate, and this unstable system, which could have survived, may cause uncontrolled cascading blackouts. In false alarm, islanding is triggered too early, and an originally stable system is forced to split into islands, resulting in unnecessary disruption and economic loss. Thus, the early recognition and identification of “the point of no return” before blackout is inevitable. The single machine equivalent (SIME) method is adopted online to predict transient stability during cascading outages that would shortly lead to blackouts, giving support in decisions about when to island in terms of transient instability. SIME also evaluates dynamic stability after islanding and ensures that the selected island candidates are stable before action is taken. Moreover, in this thesis, the power flow tracing-based method provides all possible islanding cutsets, and SIME helps to identify the one that has the best transient stability and minimal power flow disruption. If no possible island cut set exists, corrective actions through tripping critical generators or load shedding are undertaken in each island. The IEEE 10-generator, 39-busbar power system and 16-generator 68-busbar system are used to demonstrate the entire framework of the controlled islanding scheme. The performance of each methodology involved in each stage is then presented

Feasibility study of electromechanical cylinder drivetrain for offshore mechatronic systems

Currently, there is an increasing focus on the environmental impact and energy consumption of the oil and gas industry. In offshore drilling equipment, electric motors tend to replace traditionally used hydraulic motors, especially in rotational motion control applications. However, force densities available from linear hydraulic actuators are still typically higher than those of electric actuators. Therefore, usually the remaining source of hydraulic power is thereby the hydraulic cylinder. This paper presents a feasibility study on the implementation of an electromechanical cylinder drivetrain on an offshore vertical pipe handling machine. The scope of this paper is to investigate the feasibility of a commercial off-the-shelf drivetrain. With a focus on the motion performance, numerical modeling and simulation are used when sizing and selecting the components of the considered electromechanical cylinder drivetrain. The simulation results are analyzed and discussed together with a literature study regarding advantages and disadvantages of the proposed solution considering the design criteria of offshore drilling equipment. It is concluded that the selected drivetrain can only satisfy the static motion requirements since the required transmitted power is higher than the recommended permissible power of the transmission screw. Consequently, based on the recommendation of the manufacturer, avoidance of overheating cannot be guaranteed for the drivetrain combinations considered for the case study presented in this paper. Hence, to avoid overheating, the average speed of the motion cycle must be decreased. Alternatively, external cooling or temperature monitoring and control system that prevents overheating could be implemented

Cascading Outages Detection and Mitigation Tool to Prevent Major Blackouts

Due to a rise of deregulated electric market and deterioration of aged power system infrastructure, it become more difficult to deal with the grid operating contingencies. Several major blackouts in the last two decades has brought utilities to focus on development of Wide Area Monitoring, Protection and Control (WAMPAC) systems. Availability of common measurement time reference as the fundamental requirement of WAMPAC system is attained by introducing the Phasor Measurement Units, or PMUs that are taking synchronized measurements using the GPS clock signal. The PMUs can calculate time-synchronized phasor values of voltage and currents, frequency and rate of change of frequency. Such measurements, alternatively called synchrophasors, can be utilized in several applications including disturbance and islanding detection, and control schemes. In this dissertation, an integrated synchrophasor-based scheme is proposed to detect, mitigate and prevent cascading outages and severe blackouts. This integrated scheme consists of several modules. First, a fault detector based on electromechanical wave oscillations at buses equipped with PMUs is proposed. Second, a system-wide vulnerability index analysis module based on voltage and current synchrophasor measurements is proposed. Third, an islanding prediction module which utilizes an offline islanding database and an online pattern recognition neural network is proposed. Finally, as the last resort to interrupt series of cascade outages, a controlled islanding module is developed which uses spectral clustering algorithm along with power system state variable and generator coherency information

Aerospace medicine and biology: A cumulative index to the continuing bibliography of the 1973 issues

A cumulative index to the abstracts contained in Supplements 112 through 123 of Aerospace Medicine and Biology A Continuing Bibliography is presented. It includes three indexes: subject, personal author, and corporate source