Multi-mode damping control approach for the optimal resilience of renewable-rich power systems

The integration of power-electronics-based power plants is developing significantly due to the proliferation of renewable energy sources. Although this type of power plant could positively affect society in terms of clean and sustainable energy, it also brings adverse effects, especially with the stability of the power system. The lack of inertia and different dynamic characteristics are the main issues associated with power-electronics-based power plants that could affect the oscillatory behaviour of the power system. Hence, it is important to design a comprehensive damping controller to damp oscillations due to the integration of a power-electronics-based power plant. This paper proposes a damping method for enhancing the oscillatory stability performance of power systems with high penetration of renewable energy systems. A resilient wide-area multimodal controller is proposed and used in conjunction with a battery energy storage system (BESS) to enhance the damping of critical modes. The proposed control also addresses resiliency issues associated with control signals and controllers. The optimal tuning of the control parameters for this proposed controller is challenging. Hence, the firefly algorithm was considered to be the optimisation method to design the wide-area multimodal controllers for BESS, wind, and photovoltaic (PV) systems. The performance of the proposed approach was assessed using a modified version of the Java Indonesian power system under various operating conditions. Both eigenvalue analysis and time-domain simulations are considered in the analysis. A comparison with other well-known metaheuristic methods was also carried out to show the proposed method’s efficacy. Obtained results confirmed the superior performance of the proposed approach in enhancing the small-signal stability of renewable-rich power systems. They also revealed that the proposed multimodal controller could enhance the penetration of renewable energy sources in the Javan power system by up to 50%. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Simultaneous Parameter Tuning of PSS and Wide-Area POD in PV Plant using FPA

In future power grid scenario, large-scale renewable energy based on power plant will be one of the main generations. Among renewable based power plant type, large-scale photovoltaic (PV) plant becoming more popular as they could provide zero emission and sustainable energy. However, even though PV plant could contribute positive impact to the environment, they could also contribute negatively to the power system. Large-scale PV generation came with different dynamic and zero inertia characteristic due to the application of the power electronic devices. Furthermore, PV plant has also drawback in terms of intermittent power output due to the uncertainty of the sources. Those handicaps could deteriorate the stability performance of power system especially oscillatory stability. Adding power system stabilizer (PSS) to the systems is one of the approaches for handling the oscillatory stability. However, with integration of PV plant in the systems, PSS alone is not enough to handle the oscillatory problems coming from various sources such us from PV plant dynamic. Hence, utilizing wide-area power oscillation damping (POD) as PV plant supplementary controller is inevitable. Hence, this paper proposes simultaneous parameter tuning between PSS and wide-area POD in PV plant using flower pollination algorithm (FPA) as the optimization method. The two-area power system is employed to evaluate the performance of PSS and POD using FPA. From the results, it is found that the proposed method could enhance the oscillatory stability of the system

Using Particle Swarm Optimization for Power System Stabilizer and energy storage in the SMIB system under load shedding conditions

Generator instability, which manifests as oscillations in frequency and rotor angle, is brought on by sudden disruptions in the power supply. Power System Stabilizer (PSS) and Energy Storage are additional controllers that enhance generator stability. Energy storage types include superconducting magnetic (SMES) and capacitive (CES) storage. If the correct settings are employed, PSS, SMES, and CES coordination can boost system performance. It is necessary to use accurate and effective PSS, SMES, and CES tuning techniques. Artificial intelligence techniques can replace traditional trial-and-error tuning techniques and assist in adjusting controller parameters. According to this study, the PSS, SMES, and CES parameters can be optimized using a method based on particle swarm optimization (PSO). Based on the investigation's findings, PSO executes quick and accurate calculations in the fifth iteration with a fitness function value of 0.007813. The PSO aims to reduce the integral time absolute error (ITAE). With the addition of a load-shedding instance, the case study utilized the Single Machine Infinite Bus (SMIB) technology. The frequency response and rotor angle of the SMIB system are shown via time domain simulation. The analysis's findings demonstrate that the controller combination can offer stability, reducing overshoot oscillations and enabling quick settling times.

Smart DIPSS for Dynamic Stability Enchancement on Multi-Machine Power System

Disruption of the electric power system always results in instability. These disturbances can be in the form of network breaks (transients) or load changes (dynamic). Changes in load that occur suddenly and periodically cannot be responded well by the generator so that it can affect the dynamic stability of the system. This causes the occurrence of frequency oscillations in the generator. A poor response can cause frequency oscillations for a long period. This will result in a reduction in the available power transfer power. In a multi-machine power system, all the machines work in synchrony, so the generator must operate at the same frequency. Therefore, disturbances that arise will have a direct impact on changes in electrical power. In addition, changes in electrical power will have an impact on mechanical power. The difference in response speed between a fast electrical power response and a slower mechanical power response will result in instability. As a result of these differences, the system oscillates. The addition of the excitation circuit gain is less able to stabilize the system. To solve the problem, additional signal changes are required. The additional signal is generated by the Dual Input Power System Stabilizer (DIPSS) setting using the Ant Colony Optimization (ACO) method.Disruption of the electric power system always results in instability. These disturbances can be in the form of network breaks (transients) or load changes (dynamic). Changes in load that occur suddenly and periodically cannot be responded well by the generator so that it can affect the dynamic stability of the system. This causes the occurrence of frequency oscillations in the generator. A poor response can cause frequency oscillations for a long period. This will result in a reduction in the available power transfer power. In a multi-machine power system, all the machines work in synchrony, so the generator must operate at the same frequency. Therefore, disturbances that arise will have a direct impact on changes in electrical power. In addition, changes in electrical power will have an impact on mechanical power. The difference in response speed between a fast electrical power response and a slower mechanical power response will result in instability. As a result of these differences, the system oscillates. The addition of the excitation circuit gain is less able to stabilize the system. To solve the problem, additional signal changes are required. The additional signal is generated by the Dual Input Power System Stabilizer (DIPSS) setting using the Ant Colony Optimization (ACO) method

Low-Frequency Oscillation Mitigation usin an Optimal Coordination of CES and PSS based on BA

Small signal stability represents the reliability of generator for transferring electrical energy to the consumers. The stress of the generator increases proportionally with the increasing number of load demand as well as the uncertainty characteristic of the load demand. This condition makes the small signal stability performance of power system become vulnerable. This problem can be handled using power system stabilizer (PSS) which is installed in the excitation system. However, PSS alone is not enough to deal with the uncertainty of load issue because PSS supplies only an additional signal without providing extra active power to the grid. Hence, utilizing capacitor energy storage (CES) may solve the load demand and uncertainty issues. This paper proposes a coordination between CES and PSS to mitigate oscillatory behavior of the power system. Moreover, bat algorithm is used as an optimization method for designing the coordinated controller between CES and PSS. In order to assess the proposed method, a multi-machine two-area power system is applied as the test system. Eigenvalue, damping ratio, and time domain simulations are performed to examine the significant results of the proposed method. From the simulation, it is found that the present proposal is able to mitigate the oscillatory behavior of the power system by increasing damping performance from 4.9% to 59.9%

Enabling Resilient Multi-Mode Controller in Power System With Re and Bes Using Firefly Algorithm

ABSTRACT This paper proposed a damping method for enhancing oscillatory stability performance of power systems with high penetration of renewable energy by a resilient wide-area multi-mode controller. The resilient wide-area multi-mode controller is used as an additional controller in a renewable energy system with a battery energy storage to enhance the damping of the critically weak modes. The weak modes are likely to be triggered in the event of line outages or any other disturbances, and the system may become unstable in the absence of proper corrective and preventive control. A firefly algorithm has been employed to design such a controller. Eigenvalue analysis and time-domain simulation are used to analyze the performance of the proposed controller in a realistic representative power system. From the simulation results, it is evident that the oscillatory stability performance of the renewable rich power system can be enhanced with the proposed control to keep the damping on critical modes to the industrial standards. Furthermore, renewable energy penetration can be increased significantly in the realistic representative system by introducing the proposed controller without disturbing the oscillatory stability margin. INDEX TERMS: BESS; damping; eigenvalue; firefly algorithm; oscillatory stability; renewable energ

Adaptive virtual inertia controller based on machine learning for superconducting magnetic energy storage for dynamic response enhanced

The goal of this paper was to create an adaptive virtual inertia controller (VIC) for superconducting magnetic energy storage (SMES). An adaptive virtual inertia controller is designed using an extreme learning machine (ELM). The test system is a 25-bus interconnected Java Indonesian power grid. Time domain simulation is used to evaluate the effectiveness of the proposed controller method. To simulate the case study, the MATLAB/Simulink environment is used. According to the simulation results, an extreme learning machine can be used to make the virtual inertia controller adaptable to system variation. It has also been discovered that designing virtual inertia based on an extreme learning machine not only makes the VIC adaptive to any change in the system but also provides better dynamics performance when compared to other scenarios (the overshoot value of adaptive VIC is less than -5×10-5)

Particle swarm optimization and Taguchi algorithm-based power system stabilizer-effect of light loading condition

A robust design of particle swarm optimization (PSO) and Taguchi algorithm-based power system stabilizer (PSS) is presented in this paper. It incorporates a novel concept in which Taguchi and PSO techniques are integrated for stabilization of single machine infinite bus (SMIB). The system tolerates uncertainty and imprecision to a maximum extent. The proposed controller's effectiveness is proved through experiments covering light load condition using MATLAB/Simulink platform. The performance of the system is compared without PSS and with a conventional PSS. The settling time of the optimal PSS is decreased by more than 75% to conventional PSS. The study reveals that the proposed hybrid controller offers enhanced performance with respect to settling time as well as peak overshoot of the system

Low-Frequency Oscillatory Stability Study on 500 kV Java-Indonesian Electric Grid

Abstract— The Java-Indonesia 500 kV network is the biggest interconnected system in Indonesia. This system extends from the western part of Java Island to eastern part with a distance of approximately 1000 km. This long tie-line may contribute to the instability of power system, in particular, low-frequency oscillation stability of the system at weak grid condition. However, small attention has been paid so far to investigate the low-frequency oscillation stability performance on Java 500 kV system. Hence, this paper investigates the low-frequency oscillation stability performance of Java 500 kV network. Eigenvalue analysis and damping ratio analyses are employed to examine the dynamic behavior of this system. Time domain simulation has been applied to verify the modal analysis of Java system. Furthermore, dual input power system stabilizer (DIPSS) has been applied in this paper to enhance the low-frequency oscillation stability margin of the system. The DIPSS parameters are tuned using Craziness particle swarm optimization (Craziness PSO). From the simulation results, it is found that there is a weak mode in the Java 500 kV system. The installation of DIPSS based on Craziness PSO in the Java 500 system enhanced the low-frequency oscillation stability margin of the system. Keywords-Craziness PSO, damping ratio, DIPSS, Java 500 kV, time domain simulation

Optimization based Design of Dual Input PSS for Improving Small Signal Stability of Power System with RESs

Abstract: This paper proposes a method to enhance the small signal stability performance of power system considering high RESs penetration. A hybrid differential evolution-particle swarm optimization (DE-PSO) is used to design and tune DIPSS parameters. Eigenvalue, damping performance, and time domain simulation are thoroughly investigated to analyze the system performance using DIPSS based on hybrid DE-PSO and find how much RESs penetration level can be considered. From the simulation results, it is found that by utilizing DIPSS based on hybrid DE-PSO can enhance the small signal stability performance of power system with significant penetration of RESs. Keywords: Dual input power system stabilizer, metaheuristic algorithm, power system stability, renewable energy source