Effective damping of local low frequency oscillations in power systems integrated with bulk PV generation

High penetration of renewable sources into conventional power systems results in reduction of system inertia and noticeable low-frequency oscillations (LFOs) in the rotor speed of synchronous generators. In this paper, we propose effective damping of LFOs by incorporating a supplementary damping controller with a photovoltaic (PV) generating station, where the parameters of this controller are coordinated optimally with those of a power system stabilizer (PSS). The proposed method is applied to damp local electromechanical modes by studying a system comprising a synchronous generator and a PV station connected to an infinite bus. The PV station is modeled following the instructions of the Western Electricity Coordinating Council. The problem is modeled as an optimization problem, where the damping ratio of the electromechanical modes is designed as the objective function. Constraints including upper and lower limits of decision parameters and damping ratio of other modes are considered by imposing penalties on the objective function. Different optimization algorithms are used to pursue the optimal design, such as political, improved gray wolves and equilibrium optimizers. The results validate the effectiveness of the proposed controller with PSS in damping local modes of oscillations.

Robust Design of Power System Stabilizers Using Improved Harris Hawk Optimizer for Interconnected Power System

In this present work, a new metaheuristic method called a Harris hawk optimizer (HHO) is applied to achieve the optimal design of a power system stabilizer (PSS) in a multimachine power system. Several well-known chaos maps are incorporated into the HHO to form a chaotic HHO (CHHO) with the aim of improving static operators and enhancing global searching. To assess the CHHO performance, exhaustive comparison studies are made between anticipated chaotic maps in handling unconstrained mathematical problems. At this moment, The PSS design problem over a wide permutation of loading conditions is formulated as a non-linear optimization problem. The adopted objective function defines the damping ratio of lightly damped electromechanical modes subject to a set of constraints. The best PSS parameters are generated by the proposed CHHO. The applicability of the proposed CHHO based on PSS is examined and demonstrated on a 10-generator and 39-bus multimachine power system model. The performance assessments of the CHHO results are realized by a comparative study with HHO through extensive simulations along with further eigenvalue analysis to prove its efficacy. The simulation results convincingly demonstrate the high performance of the proposed CHHO-PSS under various operating scenarios.

AI-Based Damping of Electromechanical Oscillations by Using Grid-Connected Converter

The proliferation of grid-connected converter interfaced energy sources in Smart Grids, enhance sustainability and efficiency as well as minimizing power losses and costs. However, concerns arise regarding the stability and reliability of future smart grids due to this wide integration of power electronic devices, which are recognized to affect the dynamic response of the system, especially during disturbances. For instance, apart from the lower damping of existing electromechanical modes, new low-frequency oscillations begin to appear. Yet, the ability of grid-connected converters to provide grid support functionalities can alleviate the aforementioned challenges. Relevant studies show that these functionalities can be enhanced even further, if information regarding the oscillation characteristics are available. Traditional methods for extracting modal information are very well suited for monitoring purposes, however, they pose certain limitations when considered for control applications. Therefore, this paper proposes a multi-band intelligent power oscillation damper (MiPOD) that exploits 1) the inherent characteristics of grid-connected converters to damp multiple power oscillations and 2) the modeling capabilities of Artificial Intelligence (AI) for predicting the frequency of electromechanical oscillations in the system, as operating conditions change. Essentially, the MiPOD integrates the AI model in the control loop of the converter to attenuate multiple modes of oscillation. The proposed controller is validated for different disturbances and randomly generated operating points in the two area system. Specifically, in this case the AI model is a Random Forest ensemble regressor that is developed for tracking two electromechanical modes. As it is shown, the MiPOD can improve the overall performance of the system under various contingency scenarios with only 6% of the corresponding total nominal capacity of synchronous generators. In addition, the monitoring and damping abilities of the MiPOD are demonstrated for a vast range of operating points just by tuning two parameters; the predicted oscillation frequencies of the local and inter-area mode.

Hybrid Power System Design for Damping Electromechanical Modes of Oscillation in Grid Connected Machines

Due to the natural intermittent nature of wind and solar PV, autonomous wind/PV systems for renewable energy typically require energy storage or other sources of production to form a hybrid system. in this paper objective of the designing of a grid dynamics controller equipped with IGBT based bridge structure for stabilizing various electrical parameters on the grid system while its renewable energy-based grid integration. And the controller has to be designed with modulation technique, for both voltage and current at particular frequency following stabilization which is both simple in implementation and operation. And the comparative analysis of techniques used has to be carried out with AI-based optimization algorithms for studying its effectiveness. The results of the THD % of voltage in the system having no controller was found to be 3.32 %. in the system having adaptive neural PSO switching of grid dynamics controller, the distortion level came down to 1.96%. The hybrid system with solar wind energy was further integrated with the grid and was analyzed for the rotor angle stability in the two machines. It was concluded that out of the three controls for grid dynamics controller the artificial intelligence-based adaptive neural PSO switching was found to be best with maximum stability of machines.