Robust multi-machine power system stabilizer design using bio-inspired optimization techniques and their comparison

DATA AVAILABILITY : Data will be made available on request.This paper reports a comparative study among four bio-inspired meta-heuristic techniques i.e. Sooty-Tern Optimization (STO), Grey Wolf Optimization (GWO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) to tune the robust Power System Stabilizer (PSS) parameters of the multi-machine power system. These approaches are successfully tested on two bench-mark systems: sixteen-machine, sixty-eight-bus New England Extended Power Grid (NEEPG) and three-machine, nine-bus Western System Coordinating Council (WSCC). The efficacy of planned PSS via STO and GWO is validated by extensive non-linear simulations, eigenvalue analysis, and performance indices for numerous operating conditions under decisive perturbations, and outcomes are matched with those of GA and PSO techniques. In addition, the robustness is also tested for these algorithms. The results indicate that the PSS design using STO and GWO improves the small-signal stability and damping performance for mitigating inter-area and local area modes of low-frequency oscillations compared to GA and PSO.https://www.elsevier.com/locate/ijepeshj2024Electrical, Electronic and Computer EngineeringSDG-07:Affordable and clean energ

Machine Learning Applications for Dynamic Security Assessment in presence of Renewable Generation and Load Induced Variability

abstract: Large-scale blackouts that have occurred across North America in the past few decades have paved the path for substantial amount of research in the field of security assessment of the grid. With the aid of advanced technology such as phasor measurement units (PMUs), considerable work has been done involving voltage stability analysis and power system dynamic behavior analysis to ensure security and reliability of the grid. Online dynamic security assessment (DSA) analysis has been developed and applied in several power system control centers. Existing applications of DSA are limited by the assumption of simplistic load profiles, which often considers a normative day to represent an entire year. To overcome these aforementioned challenges, this research developed a novel DSA scheme to provide security prediction in real-time for load profiles corresponding to different seasons. The major contributions of this research are to (1) develop a DSA scheme incorporated with PMU data, (2) consider a comprehensive seasonal load profile, (3) account for varying penetrations of renewable generation, and (4) compare the accuracy of different machine learning (ML) algorithms for DSA. The ML algorithms that will be the focus of this study include decision trees (DTs), support vector machines (SVMs), random forests (RFs), and multilayer neural networks (MLNNs). This thesis describes the development of a novel DSA scheme using synchrophasor measurements that accounts for the load variability occurring across different seasons in a year. Different amounts of solar generation have also been incorporated in this study to account for increasing percentage of renewables in the modern grid. To account for the security of the operating conditions different ML algorithms have been trained and tested. A database of cases for different operating conditions has been developed offline that contains secure as well as insecure cases, and the ML models have been trained to classify the security or insecurity of a particular operating condition in real-time. Multiple scenarios are generated every 15 minutes for different seasons and stored in the database. The performance of this approach is tested on the IEEE-118 bus system.Dissertation/ThesisMasters Thesis Electrical Engineering 201

A smart power system stabilizer for dynamic reduction of a power system model

This thesis focuses on studying the dynamic stability of power systems and improving them by the addition of smart power system stabilizers (PSSs). A conventional design technique of a power system stabilizer that uses a single machine connected to an infinite bus through a transmission line (SMIB) has been widely used for study of elecromechanical perturbations. This approach requires estimating the external equivalent impedance and the voltage at an external bus for each machine in a multi-machine system. This study will use the conventional mathematical method, which represents a power system with some modifications. The dynamic model is linearized by taking the high voltage side on the generation unit as a reference instead of the infinite bus voltage. By using this modification, several improvements are accomplished, the main ones of which are: the estimation of states is eliminated, the time consumed in estimating calculations is reduced, the parameters of the model are independent of the external system, and the PSS design for each machine is independent in a multi-machine environment system. This strategy enables a PSS to be designed for a single machine and then implemented in a multi-machine system. Power systems have advanced to the point that they now cover vast geographical areas. Consequently, they are not only quite complicated, but the system orders are also high. As the complexity of these systems increases, so does the difficulty of examining their dynamic stability and adjusting their controllers. In this research, to address these issues, the reduced model technique has been employed to mathematically define smaller system models from existing models, such that the properties of both systems are comparable properties. The parameters of the PSS are determined based on a modified Heffron- Phillips model of the power system at certain operating conditions where it can provide reliable performance. Since the power systems are highly nonlinear with configurations and parameters that change with time, a typical PSS design, which is based on a linearized model of the power system, cannot guarantee its performance in practical operating environments. The present study attempts to overcome this limitation by implementing smart power system stabilizers. In the context of this thesis the word smart means novel technique. An artificial neural network power system stabilizer (ANN-PSS), a novel multi input fuzzy logic power system stabilizer (FLPSS), and a modified multi-resolution proportional-integral-derivative power system stabilizer (MMR-PID-PSS), based on the dynamic reduction of a power system model. These PSSs have been developed to refine the power system dynamic performance by adjusting the regulator’s parameters in real-time simulation under various operating conditions. In the first part of this research, the digital simulations results using the proposed ANN-PSS and FLPSS are carried out on a single machine connected to a network and are then compared with conventional Lead-Lag PSS. The results show that the power system with FLPSS has a better dynamic response over a wide range of operating conditions and parameter changes. Next, the digital simulations results using the proposed MMR-PID-PSS is carried out on a single machine connected to the network, a 4-machine 10-bus power system, and a 10-machine 39-bus power system and then compared with FLPSS. The results validate the effectiveness of the proposed MMR-PID-PSS regarding reduced overshoot, undershoot, and settling time under a different type of disturbances