Coordination of Power-System Stabilizers and Battery Energy-Storage System Controllers to Improve Probabilistic Small-Signal Stability Considering Integration of Renewable-Energy Resources

This paper proposes a probabilistic method to obtain optimized parameter values for different power-system controllers, such as power-system stabilizers (PSSs) and battery energy-storage systems (BESSs) to improve probabilistic small-signal stability (PSSS) considering stochastic output power due to wind- and solar-power integration. The proposed tuning method is based on a combination of an analytical method that assesses the small-signal-stability margin, and an optimization technique that utilizes this statistical information to optimally tune power-system controllers. The optimization problem is solved using a metaheuristic technique known as the firefly algorithm. Power-system stabilizers, as well as sodium–sulfur (NaS)-based BESS controllers with power-oscillation dampers (termed as BESS controllers) are modeled in detail for this purpose in DIGSILENT. The results show that the sole use of PSSs and BESS controllers is insufficient to improve dynamic stability under fluctuating input power due to the integration of renewable-energy resources. However, the proposed strategy of using BESS and PSS controllers in a coordinated manner is highly successful in enhancing PSSS under renewable-energy-resource integration and under different critical conditions.

An Objective Approach to Identifying Diagnostic Expertise Among Power System Controllers

Objective: The present study investigated whether performance across a range of cue-based cognitive tasks differentiated the diagnostic performance of power control operators into three distinct groups, characteristic of novice, competence, and expertise. Background: Despite its increasing importance in the contemporary workplace, there is little understanding of the cognitive processes that distinguish novice, competent, and expert performance in the context of remote diagnosis. However, recent evidence suggests that cue acquisition and utilization may represent a mechanism by which the transition from novice to expertise occurs. Method: The study involved the application of four distinct cue-based tasks within the context of power system control. A total of 65 controllers, encompassing a range of industry experience, completed the tasks as part of an in-service training program. Results: Using a cluster analysis, it was possible to extract three distinct groups of operators on the basis of their performance in the cue-based tasks, and these groups corresponded to differences in diagnostic performance. Conclusion: The results indicate assessments of the capacity to extract and utilize cues were able to distinguish expert from competent practitioners in the context of power control. Application: Assessments of the capacity to extract and utilize cues may be used in the future to distinguish expert from nonexpert practitioners, particularly in the context of remote diagnosis.

Calculation of parameter ranges for robust gain tuning of power system controllers

This work proposes a computational tool to assist power system engineers in the field tuning of power system stabilizers (PSSs) and Automatic Voltage Regulators (AVRs). The outcome of this tool is a range of gain values for theses controllers within which there is a theoretical guarantee of stability for the closed-loop system. This range is given as a set of limit values for the static gains of the controllers of interest, in such a way that the engineer responsible for the field tuning of PSSs and/or AVRs can be confident with respect to system stability when adjusting the corresponding static gains within this range. This feature of the proposed tool is highly desirable from a practical viewpoint, since the PSS and AVR commissioning stage always involve some readjustment of the controller gains to account for the differences between the nominal model and the actual behavior of the system. By capturing these differences as uncertainties in the model, this computational tool is able to guarantee stability for the whole uncertain model using an approach based on linear matrix inequalities. It is also important to remark that the tool proposed in this paper can also be applied to other types of parameters of either PSSs or Power Oscillation Dampers, as well as other types of controllers (such as speed governors, for example). To show its effectiveness, applications of the proposed tool to two benchmarks for small signal stability studies are presented at the end of this paper.