Power System Stability Control Research Articles

Power System Stability Improvement of FACTS Controller and PSS Design: A Time-Delay Approach

The existence of low-frequency oscillations in power systems is the cause of power angle instability, limiting the transmission of maximum tie-line power. One of the effective ways to improve the stability limits is by installing a power system stabilizer and supplementary excitation control to augment with an automatic voltage regulator (AVR) supplemental feedback stabilizing signal. This paper proposes a new strategy for simultaneously tuning the power system stabilizer (PSS) and FACTS controller, considering time delays. The design of the proposed controller is modeled as an optimization problem, and the parameters of the controller are optimized through the grasshopper optimization algorithm (GOA). The suggested controller’s efficacy is evaluated for both single-machine infinite bus systems and multi-machine power systems under various disturbances. It also investigated the performance of the proposed controller with variations in signal transmission delays. The results obtained from GOA optimized proposed controller are compared with those obtained from the differential evolution algorithm, genetic algorithm, and whale optimization algorithm. In this context, the proposed GOA optimized controller reduced the objective function value by 16.32%, 14.56%, and 13.72%, respectively, in the SMIB system and 1.41%, 9.98%, and 13.31%, respectively, for the multi-machine system compared with the recently published WOA, and the well-established GA and DE. Further, the proposed controller is found to be stable and effectively increases stability even under small disturbances.

Enhancing wind power forecasting from meteorological parameters using machine learning models

Short-term wind power forecasting is crucial for power system stability, dispatching, and cost control. Wind energy has the potential to be a viable source of renewable energy. Wind power generation forecasting is vital for resolving the supply and demand challenges of the smart grid. Moreover, one of the most problematic aspects of wind power is its high fluctuation and intermittent nature, which makes forecasting difficult. The goal of this research is to create machine learning models that can properly estimate wind power production. Significantly, the major contributions of this work are highlighted in the following significant elements. First, a data analysis framework for visualizing the gathered dataset from the SCADA system is presented. Second, for forecasting wind power time-series dataset values, we examine the predicting performance of various machine learning models using various statistical indices. The experimental findings demonstrate that with a minor reconstruction error, the proposed forecast approaches can minimize the complexity of the forecasting. Furthermore, in terms of forecast accuracy, a gradient boosting regression model outperforms other benchmark models. According to the analysis, our methodology might be applied in real-world circumstances to assist the management group in regulating the power provided by wind turbines.

SCADA system dataset exploration and machine learning based forecast for wind turbines

Effective short-term wind power forecast is essential for adequate power system stability, dispatching and cost control. There are various significant renewable energy sources available, including wind power, which is one of the most favorable power source. As a result, wind power is an important green form of electricity generation. Wind power prediction is a crucial topic in reducing the energy system's unpredictability. The act of balancing energy supply and demand is critical. The main aim of this research is to offer a time-series dataset-based prediction tool for estimating wind power. In addition, for visualizing the dataset obtained from the SCADA (Supervisory Control and Data Acquisition) system, dataset exploration approach is also presented. The dataset analysis approach are shown in polar coordinate systems and pair plot diagram for better understanding of the relation between wind and power production relationship. With the help of given input features i.e. theoretical power, produced active power, wind direction, wind speed, month and hour, generated turbine power is forecasted using various machine learning algorithms. The performance of the developed model has been assessed using several statistical criteria. The experimental findings reveal that the XGBoost regression approach has greater prediction accuracy than the other methods with R2 of around 0.969, MSE of 0.003, RMSE of 0.064, MAPE of 0.282, and MAE of 0.026.

Hybrid Algorithm for Coordinated Design of PSSs and SVC

The assessment of new coordinated design of Power System Stabilizers (PSSs) and Static Var Compensator (SVC) in a multimachine power system via statistical method is proposed in this paper. The coordinated design problem of PSSs and SVC over a wide range of loading conditions is formulated as an optimization problem. The Bacterial Swarming Optimization (BSO), which synergistically couples the Bacterial Foraging (BF) with the Particle Swarm Optimization (PSO), is employed to search for optimal controllers parameters. By minimizing the proposed objective function, in which the speed deviations between generators are involved; stability performance of the system is improved. To compare the capability of PSS and SVC, both are designed independently, and then in a coordinated manner. Simultaneous tuning of the BSO based coordinated controller gives robust damping performance over wide range of operating conditions and large disturbance in compare to optimized PSS controller based on BSO (BSOPSS) and optimized SVC controller based on BSO (BSOSVC). Moreover, a statistical T test is performed to ensure the effectiveness of coordinated controller versus uncoordinated one.

Coordinated Wide-Area Damping Control in Modern Power Systems Embedded with Utility-Scale Wind-Solar Plants

This paper presents a coordinated wide-area damping control of power system stabilizers (PSSs), static synchronous compensator (STATCOM), and static synchronous series compensator (SSSC) for grid-forming based wind and solar PV generation resources. The main objective of this work is to enhance the damping for inter-area oscillations while maintaining the voltage and frequency in the prescribed range, taking into consideration the operational uncertainties. Better voltage and reactive power support are provided by the use of STATCOM and for additional damping, an SSSC is employed with the coordinated wide-area damping controller (CWADC). Variable-speed wind turbine control methodology strategically combined with inertia control, de-loading control, pitch angle control, and electronic rotor speed control is employed to obtain an optimum response for discontinuous generation mixes and variable wind speed. The geometric measures of controllability/observability and residues method have been utilized to choose the appropriate input control signals and optimal location of CWADC. A multi-objective salp swarm algorithm has been used to optimize the parameters of the proposed CWADC. To demonstrate the efficacy of the proposed CWADC, nonlinear time-domain simulations are performed on a modified IEEE 68-bus test system embedded with hybrid wind-solar power plants considered multiple time delays.

Coordinated Design of Type-2 Fuzzy Lead–Lag-Structured SSSCs and PSSs for Power System Stability Improvement

This work suggests a type-2 fuzzy lead–lag (T2FLL) controller structure for flexible AC transmission system (FACTS)-based damping controllers and power system stabilizers (PSSs) for power system stability improvement. The values of the suggested controller are optimized by a hybrid adaptive differential evolution and pattern search algorithm (hADE-PS) method. Initially, a single-machine infinite-bus (SMIB) system with lead–lag (LL)-structured FACTS and PSS controllers is considered, and the dominance of the hADE-PS method is established over the original differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO). The supremacy of T2FLL over the lead–lag (LL) controller is established under different large and small disturbance conditions, as well as varied loading conditions and fault positions. Lastly, the effectiveness of T2FLL is evaluated in a multimachine power system (MMPS). It is demonstrated that the suggested T2FLL offers better performance than the LL controller under various large and small disturbance conditions by providing significantly more damping to all modes of oscillations.

Mitigation of Low-Frequency Oscillation in Power Systems through Optimal Design of Power System Stabilizer Employing ALO

Low-frequency oscillations are an inevitable phenomenon of a power system. This paper proposes an Ant lion optimization approach to optimize the dual-input power system stabilizer (PSS2B) parameters to enhance the transfer capability of the 400 kV line in the North-West region of the Ethiopian electric network by the damping of low-frequency oscillation. Double-input Power system stabilizers (PSSs) are currently used in power systems to damp out low-frequency oscillations. The gained minimum damping ratio and eigenvalue results of the proposed Ant lion algorithm (ALO) approach are compared with the existing conventional system to get better efficiency at various loading conditions. Additionally, the proposed Ant lion optimization approach requires minimal time to estimate the key parameters of the power oscillation damper (POD). Consequently, the average time taken to optimally size the parameters of the PSS controller was 14.6 s, which is pretty small and indicates real-time implementation of an ALO developed model. The nonlinear equations that represent the system have been linearized and then placed in state-space form in order to study and analyze the dynamic performance of the system by damping out low-frequency oscillation problems. Finally, conventional fixed-gain PSS improves the maximum overshoot by 5.2% and settling time by 51.4%, but the proposed optimally sized PSS employed with the ALO method had improved the maximum overshoot by 16.86% and settling time by 78.7%.

Stability improvement of MMC‐based hybrid AC/DC grids through the application of a decentralized optimal controller

Interconnection and expansion of AC networks through high-voltage direct current grids based on modular multilevel converters to form a multiterminal hybrid AC/DC grid can pose stability issues. These challenges can arise from dynamic interactions between/within AC and DC subgrids due to poorly damped modes that are potential sources of persistent and disruptive oscillations. This paper aims to ensure the stability of multiterminal hybrid AC/DC grids via a decentralized optimal controller. The proposed methodology analytically and simultaneously identifies both the decentralized optimal controller and the worst-case perturbation scenario under the grid control inputs' and state variables' constraints, without the need for detailed and time-consuming dynamic simulations of all possible scenarios. Eigenvalue stability analysis and time-domain simulations show that the proposed controller can efficiently enhance the test grid stability margins and reduce the oscillations, not only under the worst-case perturbation scenario (increasing damping ratios of the two pairs of least damped modes by 2.34 and 4.05 times) but also under other critical fault conditions. Furthermore, the controller's superior performance is validated through comparison with the power system stabilizer and modular multilevel converter droop controller under small and large disturbances, and its robustness is assessed against parametric uncertainties.

Mayfly Optimization Algorithm Applied to the Design of PSS and SSSC-POD Controllers for Damping Low-Frequency Oscillations in Power Systems

In this paper, it is proposed to apply the mayfly optimization algorithm (MOA) to perform the coordinated and simultaneous tuning of the parameters of supplementary damping controllers, i.e., power system stabilizer (PSS) and power oscillation damping (POD), that actuate together with the automatic voltage regulators of the synchronous generators and the static synchronous series compensator (SSSC), respectively, for damping low-frequency oscillations in power systems. The performance of the MOA is compared with the performances of the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm for solving this problem. The dynamics of the power system is represented using the current sensitivity model, and, because of that, a current injections model is proposed for the SSSC, which uses proportional-integral (PI) controllers and the residues of the current injections at the buses, obtained from the Newton–Raphson method. Tests were carried out using the New England system and the two-area symmetrical system. Both static and dynamic analyses of the operation of the SSSC were performed. To validate the proposed optimization techniques, two sets of tests were conducted: first, with the purpose of verifying the performance of the most effective algorithm for tuning the parameters of PSSs, PI, and POD controllers, and second, with the purpose of performing studies focused on small-signal stability. The results have validated the current injections model for the SSSC, as well as have indicated the superior performance of the MOA for solving the problem, accrediting it as a powerful tool for small-signal stability studies in power systems.

Control and modelling of multi-machine power system stabilizer with FACTS

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Optimal Placement of FACTS Devices Using Filter Feeding Allogenic Engineering Algorithm

Flexible Alternating Current Transmission System (FACTS) are a useful tool for power system stability improvement and control. This research developed a new algorithm for optimal placement of FACTS devices named the Filter Feeding Allogenic Engineering (FFAE) algorithm inspired by the filter feeding and the motile behaviour of Allogenic Engineers.Kenya’s 87-Bus, 25-Generator, 132 kV and 220 kV transmission network was used as the test case in Matlab’s environment. The said algorithm was used to solve the twin objectives on minimization of active power loss and voltage deviations. One meta-heuristic technique-the whale optimization algorithm (WOA)-and one sensitivity based method-the fast voltage stability index (FVSI)—were used to test the accuracy of the proposed method. The computation time was 0.283 s and 0.308 s for FFAE and the whale optimization algorithm respectively mainly because of the use of Kalman filtration to achieve targeted iterations and using a more robust algorithm. The FFAE algorithm summed the total active power losses to 99.911% whereas the whale optimization algorithm gave a total of 99.770% indicating superior computational ability of the new method.

Real Power Modulation Strategies for Transient Stability Control

Transient stability control of power systems is based on actions that are taken automatically following a disturbance to ensure that the system remains in synchronism. Examples of such measures include generator rejection and the insertion of dynamic braking resistors. Methods like these are designed to rapidly absorb excess energy or otherwise alter the generation-demand balance at key points in the system. While these methods are often effective, they lack the ability to inject real power to compensate for a deficit. Utility-scale inverter-based resources, particularly energy storage systems, enable bidirectional modulation of real power with the bandwidth necessary to provide synchronizing torque. These resources, and the control strategies they enable, have garnered substantial research interest. This paper provides a critical review of research on real power modulation strategies for transient stability control. The design of these control strategies is heavily informed by the methods used to assess changes in the transient stability margins. Rigorously assessing these changes is difficult because the dynamics of large-scale power systems are inherently nonlinear. The well-known equal-area criterion is physically intuitive, but conceptual extensions are necessary for multi-machine systems. So-called direct methods of transient stability analysis offer a more general alternative; however, these methods require many simplifying assumptions and have difficulty incorporating detailed system dynamics. In this paper, we discuss data-driven methods for offline stability assessment based on Koopman operator theory.

Optimal tuning of fractional‐order proportional, integral, derivative and tilt‐integral‐derivative based power system stabilizers using Runge Kutta optimizer

AbstractLow‐frequency power system oscillation is of great concern as it may lead to power system instability. Moreover, this action will lead to the abate capability of electric power transfer. By introducing a stabilizing signal into the excitation system, it was possible to improve the damping in the system. The power system stabilizer (PSS) provides this signal. This manuscript aims to find the optimal tuning of three different PSS controllers using a recent optimization algorithm called Runge Kutta optimizer (RUN). Based on the obtained results, the RUN shows superiority and fast convergence over competitive algorithms. In addition, the fractional‐order proportional, integral, derivative (FOPID) and tilt‐integral‐derivative (TID) based PSS shows great damping capability over the lead–lag, PI, and FOPID‐based PSS. Also, the TID‐based PSS achieves better performance indices in terms of the lowest maximum overshoot and minimum settling time. Hence, it is recommended for PSS based controller.

Sliding Mode Self-Tuned Single Neuron PID Controller for Power System Stabilizer

In this paper, a modified technique based on the combination of the Single Neuron PID (SNPID), as the main controller and Sliding Mode Control (SMC), as an adaptation technique, to design an optimized self-tuned for SNPID controller that may overcome difficulties faced when a change in system operating points occurs. The proposed approach has been implemented as a power system stabilizer (PSS) for a synchronous generator connected to an infinite bus. The Flower Pollination (FP) optimization is based on an appropriate objective function. To demonstrate the effectiveness of the combination obtained controllers, PSS, is tested under different operating conditions. The combination controllers are shown through uncertainties system parameters changes under different disturbances. The results show the ability of the suggested controllers to enhance well the system performances

Control of Low-Frequency Oscillation on Electrical Power System Under Large EV-Charging Station Installation Using PSO Technique for Turning PSS Parameters

This paper presents a low-frequency oscillation compensation in an electrical power system connecting large-scale electric vehicle loads (EVs). The Particle Swarm Optimization (PSO) method has been adapted to adjust the control parameters of the generator under Power System Stabilizer (PSS) parameters. The EVs charging stations have been directly installed to the power grid for the battery charging process of the EVs according to the increasing number of EVs. The DC fast charging of the large scale EVs has been significantly affected by the low-frequency oscillation of the grids that have been considered in terms of power system stability margin. This paper has aimed to solve the methodology for turning the PSS parameters by adapting the PSO via MATLAB® and DIgSILENT®. The PSS could control the low-frequency oscillation in the proposed power grids. The simulation system has selected the 6-bus testing system, which has increased the EVs loads by 50, 100, 150, 200, and 300 percentages at bus No. B3. The simulation results have indicated that the rotor low-frequency oscillation could control instability region. Therefore, the proposed PSS controller turning could handle the power system swing and adjust the power system stability. In this condition, the proposed PSO method should have been explicitly used for low-frequency swing control of the generator's rotor into a balanced state under the large-scale EVs penetration level.

Optimal Coordination of PSS and SSSC Controllers in Power System Using Ant Colony Optimization Algorithm

Volatility leads to disruption in synchronism between generators of a continuous system. The frequency of the volatility is usually between a few tenths of Hz to several Hz. This volatility is sometimes divided into two types, local and interregional. Local volatility is the low-frequency volatility of a power plant unit or units of a power plant relative to the grid whereas interregional volatility is the volatility of the units of one area relative to the units of another area. The worst kind of low-frequency volatility occurs when the power system in a region has a short three-phase connection to the earth, creating a complete instability of the grid and operating protective systems. One of the ways to improve the dynamic stability and steady-state of the power system is to use power system stabilizers and FACTS devices in the system. In this paper, the stabilization of the power system stabilizers (PSSs) and SSSC is done using the ant colony algorithm. Studies on a four-machine system with the three-phase error were performed in two scenarios and finally compared with the PSO method. The simulation results show that the proposed method produced more accurate performance.

Robust control of power system stabilizer using sliding mode approach

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Simultaneous design of fuzzy PSS and fuzzy STATCOM controllers for power system stability enhancement

The low frequency oscillations have always been the main problem of power system and can lead to power angle instability, limiting the maximum power to be transmitted on tie-lines and system separation. For boosting power system stability limits, the most effectiveness way is to install supplementary excitation control, power system stabilizer (PSS) to add a supplementary feedback stabilizing signal into the automatic voltage regulator (AVR). This article investigates the coordination and optimization of fuzzy controllers for designing PSS and STATCOM controllers for more attenuation of power system fluctuations. The designed fuzzy controller replaces the STATCOM AC voltage regulator. Moreover, for more damping a fuzzy power system stabilizer (FPSS) is placed on all machines. Coordination between Fuzzy Based STATCOM (FSTATCOM) and FPSS is achieved by Self-Adaptive Learning Bat Algorithm (SALBA) in two stages. At first, scaling factors and then fuzzy sets of membership functions (MFs) will be tuned based on a performance index. To indicate the effectiveness of the proposed scheme, the coordinated optimized FPSS and FSTATCOM are compared with conventional design approaches like conventional PSS (CPSS) and proportional-integral controller based STATCOM (PISTATCOM). The simulations clearly demonstrate the effectiveness of the coordinated fuzzy controllers in terms of transient and dynamic stability.

Intelligent Control of Power System Stabilizer Based on Archimedes Optimization Algorithm – Feed Forward Neural Network

Intelligent Control of Power System Stabilizer Based on Archimedes Optimization Algorithm – Feed Forward Neural Network

Small-signal stability modeling of the microgrid with network transients taken into consideration

The development of a small signal model that accurately reflects dynamic processes plays an essential role in the stability analysis and control of power systems. The main components in a microgrid power system are synchronous generators, the electrical network, electrical loads, and inverters. A method to derive the microgrid state-space model is proposed in the article. This method is based on linearized models of synchronous generators, electronic power inverters, networks, and loads. This model can be further developed to account for microgrid control schemes such as frequency control and voltage regulation. A small-signal analysis of the Microgrid model is also carried out in this work.