Breaker to Control Center Integration & Automation: Protection, Control, Operation & Optimization

Recent technological advances in protection, control and optimization are enabling a more automated power system. This paper proposes the use of these technologies towards an integrated and seamless infrastructure for protection, control and operation. This infrastructure is the basis for accommodating and providing robust solutions to new problems arising from the integration of renewables, namely more uncertainty and steeper ramp rates. At the lower level we propose a dynamic state estimation of a protection zone (EBP) for the purpose of providing protection for the zone. The estimation based protection (EBP) provides the real time dynamic model of the zone as well as the real time operating conditions. Since protection is ubiquitous, it can cover the full system. We assume that GPS synchronization of the EBP is available providing accurate time tags for the real time model and operating conditions. The real time model and operating conditions can extent from the “turbine to the toaster”. We propose a methodology for automatically constructing the power system state locally and centrally at the control center with distributed controls as well as centralized controls depending on the application. For example, the centralized \ system wide real time model is used to perform system optimization functions, and then send commands back through the same communication structure to specific power system components. Since protection is ubiquitous and the modern power system has several layers of communication infrastructure, the proposed approach is realizable with very small investment. The availability of the real time dynamic model and state locally and centrally enables the seamless integration of applications. Three applications are discussed in the paper: (a) setting-less protection, (b) voltage/var control and (c) feeder load flexibility scheduling. The proposed approach and infrastructure can form the basis for the next generation of Energy Management Systems.

A Universal Grid-forming Inverter Model and Simulation-based Characterization Across Timescales

The evolution of the power grid has given rise to a variety of innovations in inverter control architectures. Among these advances, a class of controllers has emerged with the aim of enabling 100\% inverter-based grids and these are known as grid-forming methods. Since these strategies are still under active development, well validated models are needed by equipment manufacturers as well as system planners and operators. In particular, a system operator may be unable to determine specifications and services that are required from grid forming devices without having the ability to represent them in a simulation environment with trusted models. A universal grid forming model that is portable across multiple simulation domains will be valuable in addressing this issue. In this paper, we develop a practical implementation of such a model that has the ability to represent four different grid-forming methods in a variety of simulation software packages while accurately capturing dynamics across from microsecond to millisecond timescales

Time-Synchronized Full System State Estimation Considering Practical Implementation Challenges

As phasor measurement units (PMUs) are usually placed on the highest voltage buses, many lower voltage levels of the bulk power system are not observed by them. This lack of visibility makes time-synchronized state estimation of the full system a challenging problem. We propose a Deep Neural network-based State Estimator (DeNSE) to overcome this problem. The DeNSE employs a Bayesian framework to indirectly combine inferences drawn from slow timescale but widespread supervisory control and data acquisition (SCADA) data with fast timescale but local PMU data to attain sub-second situational awareness of the entire system. The practical utility of the proposed approach is demonstrated by considering topology changes, non-Gaussian measurement noise, and bad data detection and correction. The results obtained using the IEEE 118-bus system show the superiority of the DeNSE over a purely SCADA state estimator, a SCADA-PMU hybrid state estimator, and a PMU-only linear state estimator from a techno-economic viability perspective. Lastly, the scalability of the DeNSE is proven by performing state estimation on a large and realistic 2000-bus Synthetic Texas system

Integrated Centralized Substation Protection

Substation cyber assets are mission critical for protection and control of substations. Managing and ensuring their secure operation is of paramount importance. A known vulnerability is hidden failures which are responsible for about 10% of mis-operations and their detrimental effects on system reliability. The paper presents an integrated centralized substation protection approach that is based on the recently developed setting-less relays which are integrated into a centralized substation protection scheme with the following features: (a) fast, dependable and secure protection of each substation protection zone by a settingless relay, (b) supervision of each settingless relay by validating relay input data by a substation wide state estimator, (c) self-healing against hidden failures by detecting and identifying compromised data and replacing them with estimated values, thus ensuring that the settingless relays will always operate on validated data. The paper provides a summary review of the settingless protective relay and introduces the Integrated Centralized Substation Protection Scheme (ICSP) which uses the data from all settingless relays in the substation to perform a substation wide state estimation. The state estimator uses a hypothesis testing algorithm to determine whether (a) data are valid with no faults or hidden failures, (b) data are valid and a fault exists in the system, or (c) some data are invalid due to hidden failures. In the last case, the state estimator uses the substation state and model to replace the compromised data with estimated values and thus enabling self-immunization against hidden failures. A byproduct of the method is the substation state estimate which is transmitted to the control center where it is used with the state from all substations to synthesize the system wide state estimate and model. Architectural issues are addressed as well as migration issues of existing systems into the proposed ICSP

Data-Driven Fast Frequency Control using Inverter-Based Resources

We develop and test a data-driven and area-based fast frequency control scheme, which rapidly redispatches inverter-based resources to compensate for local power imbalances within the bulk power system. The approach requires no explicit system model information, relying only on historical measurement sequences for the computation of control actions. Our technical approach fuses developments in low-gain estimator design and data-driven control to provide a model-free and practical solution for fast frequency control. Theoretical results and extensive simulation scenarios on a three area system are provided to support the approach.Comment: In proceedings of the 11th Bulk Power Systems Dynamics and Control Symposium (IREP 2022), July 25-30, 2022, Banff, Canad

Autonomous Multi-Stage Flexible OPF for Active Distribution Systems with DERs

The variability of renewable resources creates challenges in the operation and control of power systems. One way to cope with this issue is to use the flexibility of customer resources in addition to utility resources to mitigate this variability. We present an approach that autonomously optimizes the available distributed energy resources (DERs) of the system to optimally balance generation and load and/or levelize the voltage profile. The method uses a dynamic state estimator which is continuously running on the system providing the real-time dynamic model of the system and operating condition. At user selected time intervals, the real-time model and operating condition is used to autonomously assemble a multi-stage optimal power flow in which customer energy resources are represented with their controls, allowing the use of customer flexibility to be part of the solution. Customer DERs may include photovoltaic rooftops with controllable inverters, batteries, thermostatically controlled loads, smart appliances, etc. The paper describes the autonomous formation of the Multi-Stage Flexible Optimal Power Flow and the solution of the problem, and presents sample results

Data-Driven Fast Frequency Control using Inverter-Based Resources

To address the control challenges associated with the increasing share of inverter-connected renewable energy resources, this paper proposes a direct data-driven approach for fast frequency control in the bulk power system. The proposed control scheme partitions the power system into control areas, and leverages local dispatchable inverter-based resources to rapidly mitigate local power imbalances upon events. The controller design is based directly on historical measurement sequences, and does not require identification of a parametric power system model. Theoretical results are provided to support the approach. Simulation studies on a nonlinear three-area test system demonstrate that the controller provides fast and localized frequency control under several types of contingencies

Breaker to Control Center Integrated Protection, Control and Operations Model

Technological advances in electric energy system data acquisition systems, time synchronization, and cyber assets used in power system substations, distribution systems, and control centers offer new opportunities to dramatically improve the practice of monitoring, protection, control, and operation of the system. We can make the computer based new technologies smarter and more intelligent to fully automate the basic protection and control functions. The challenges posed to the system from the continuous deployment of renewable resources that are typically inverter interface resources require monitoring of the system at much higher rates and development of protection and control systems that can respond in much faster rates than for conventional systems and they are immune to the characteristics of the new system, namely reduced fault currents and suppressed negative and zero sequence components of the fault currents. We propose a new system that provides validated data at fast rates (once per cycle), protective relays that are immune to the effects of inverter interfaced generation, detect anomalies, and enable the continuous operation of relays and other functions even in the presence of hidden failures in instrumentation. This system will be able to enable the operators to meet the challenges posed by the evolving power system and provides robust solutions to the new requirements

Measurement-based correlation approach for power system dynamic response estimation

Understanding power system dynamics is essential for online stability assessment and control applications. Global positioning system-synchronised phasor measurement units and frequency disturbance recorders (FDRs) make power system dynamics visible and deliver an accurate picture of the overall operation condition to system operators. However, in the actual field implementations, some measurement data can be inaccessible for various reasons, for example, most notably failure of communication. In this study, a measurement-based approach is proposed to estimate the missing power system dynamics. Specifically, a correlation coefficient index is proposed to describe the correlation relationship between different measurements. Then, the auto-regressive with exogenous input identification model is employed to estimate the missing system dynamic response. The US Eastern Interconnection is utilised in this study as a case study. The robustness of the correlation approach is verified by a wide variety of case studies as well. Finally, the proposed correlation approach is applied to the real FDR data for power system dynamic response estimation. The results indicate that the correlation approach could help select better input locations and thus improve the response estimation accuracy

ARMAX-based transfer function model identification using wide-area measurement for adaptive and coordinated damping control

One of the main drawbacks of the existing oscillation damping controllers that are designed based on offline dynamic models is adaptivity to the power system operating condition. With the increasing availability of wide-area measurements and the rapid development of system identification techniques, it is possible to identify a measurement-based transfer function model online that can be used to tune the oscillation damping controller. Such a model could capture all dominant oscillation modes for adaptive and coordinated oscillation damping control. This paper describes a comprehensive approach to identify a low-order transfer function model of a power system using a multi-input multi-output (MIMO) autoregressive moving average exogenous (ARMAX) model. This methodology consists of five steps: 1) input selection; 2) output selection; 3) identification trigger; 4) model estimation; and 5) model validation. The proposed method is validated by using ambient data and ring-down data in the 16-machine 68-bus Northeast Power Coordinating Council system. The results demonstrate that the measurementbased model using MIMO ARMAX can capture all the dominant oscillation modes. Compared with the MIMO subspace state space model, the MIMO ARMAX model has equivalent accuracy but lower order and improved computational efficiency. The proposed model can be applied for adaptive and coordinated oscillation damping control