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

Optimal Power Flow using Fuzzy-Firefly Algorithm

Development of Metaheuristic Algorithm in engineering problems grows really fast. This algorithm is commonly used in optimization problems. One of the metaheuristic algorithms is called Firefly Algorithm (FA). Firefly Algorithm is a nature-inspired algorithm that is derived from the characteristic of fireflies. Firefly Algorithm can be used to solve optimal power flow (OPF) problem in power system. To get the best performance, firefly algorithm can be combined with fuzzy logic. This research presents the application of hybrid fuzzy logic and firefly algorithm to solve optimal power flow. The simulation is done using the MATLAB environment. The simulations show that by using the fuzzy-firefly algorithm, the power losses, as well as the total cost, can be reduced significantly

Small-disturbance Angle Stability Enhancement using Intelligent Redox Flow Batteries

Small-disturbance angle stability or low-frequency oscillation is one of the important stability in the power system. Although damper windings and power system stabilizer (PSS) have been proved to stabilize and improve small-disturbance angle stability. However, due to increasing demand in the recent years, adding redox flow batteries (RFB) as additional devices is crucial. This paper investigates, the utilization additional devices called RFB to enhance the small-disturbance angle stability in the power system. Furthermore, ant colony optimization (ACO) method is used to tune RFB parameter. To analyze the stability improvement on the power system, single machine infinite bus is used as a test system. Eigenvalue and time domain simulation is used to examine the behavior of the investigated system. From the simulation, it is found that by installing RFB in the system, the small-disturbance angle stability of power system is improved and ACO can be a solution of tune RFB parameter

Improvement of voltage profile for large scale power system using soft computing approach

In modern power system operation and planning, reactive power is an important part of power system operation to supply electrical load such as an electric motor. However, the reactive current that flows from the generator to load demand can cause voltage drop and active power loss. Hence, it is essential to install a compensating device such as a shunt capacitor close to the load bus to reduce the total power loss of the transmission line and improve the voltage stability of the system. This paper presents the application of a genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC)) to obtain the optimal size of the shunt capacitor where those capacitors are located on the critical bus. To examine the efficacy of the proposed algorithm, Java-Madura-Bali (JAMALI) 500kV power system grid is used as the test system. From the simulation results, the use of PSO and ABC algorithms to obtain the sizing of the capacitor’s capacity can reduce the power loss of around 15.873 MW. Moreover, a different result is showed by the GA approach where the power loss in the JAMALI 500kV power grid can be compressed only up to 15.54 MW or 11.38% from the power system operation without a shunt capacitor. The three soft computing techniques could also maintain the voltage profile within 1.05 p.u and 0.95 p.u

LQR Tuning Using AIS for Frequency Oscillation Damping

Commonly, primary control, i.e. governor, in the generation unit had been employed to stabilize the change of frequency due to the change of electrical load during system operation. But, the drawback of the primary control was it could not return the frequency to its nominal value when the disturbance was occurred. Thus, the aim of the primary control was only stabilizing the frequency to reach its new value after there were load changes. Therefore, the LQR control is employed as a supplementary control called Load Frequency Control (LFC) to restore and keep the frequency on its nominal value after load changes occurred on the power system grid. However, since the LQR control parameters were commonly adjusted based on classical or Trial-Error Method (TEM), it was incapable of obtaining good dynamic performance for a wide range of operating conditions and various load change scenarios. To overcome this problem, this paper proposed an Artificial Immune System (AIS) via clonal selection to automatically adjust the weighting matrices, Q and R, of LQR related to various system operating conditions changes. The efficacy of the proposed control scheme was tested on a two-area power system network. The obtained simulation results have shown that the proposed method could reduce the settling time and the overshoot of frequency oscillation, which is better than conventional LQR optimal control and without LQR optimal control

All-terrain mobile robot desinfectant sprayer to decrease the spread of COVID-19 in open area

The application of disinfection is becoming popular in recent months due to the COVID-19. Usually, the disinfection is used by spraying the liquid into an object. However, the disinfection process for humans and objects in the human environment is still done manually and takes time and increases exposure to viruses. Robotic technology can be a solution to handle that problem. Following that problem, robot design is proposed with many abilities and features. The robot can operate in remote conditions and full function for approximately 56 minutes and spray the liquid for more than 1 meter. This research can effectively be applied in COVID-19 handlings

Coordination of blade pitch controller and battery energy storage using firefly algorithm for frequency stabilization in wind power systems

Utilization of renewable energy sources (RESs) to generate electricity is increasing significantly in recent years due to global warming situation all over the world. Among RESs type, wind energy is becoming more favorable due to its sustainability and environmentally friendly characteristics. Although wind power system provides a promising solution to prevent global warming, they also contribute to the instability of the power system, especially in frequency stability due to uncertainty characteristic of the sources (wind speed). Hence, coordinated controller between blade pitch controller and battery energy storage (BES) system to enhance the frequency performance of wind power system is proposed in this work. Firefly algorithm (FA) is used as optimization method for achieving better coordination. From the investigated test systems, the frequency performance of wind power system can be increased by applying the proposed method. It is noticeable that by applying coordinated controller between blade pitch angle controller and battery energy storage using firefly algorithm the overshoot of the frequency can be reduced up to -0.2141 pu and accelerate the settling time up to 40.14 second

Coordination of SPS and CES to Mitigate Oscillatory Condition on Power Systems

Oscillatory condition on power system (low-frequency oscillation) is one of the important factors to determine the quality of the power system. With the increasing number of load demand, this condition is getting worse in recent years. Hence, utilizing addition devices to maintain and mitigate the oscillatory condition of power system is crucial. This paper proposed a method to mitigate power system oscillation by installing one of the flexible AC transmission system (FACTS) devices called solid phase shifter (SPS) and energy storage devices called capacitor energy storage (CES). To analyze the performance of power system with SPS and CES, the eigenvalue and damping ratio analysis are used. Time domain simulation is also investigated to analyze the dynamic behaviors of power system considering SPS and CES. Furthermore, increasing number of load demand is carried out to analyze how much load can be increased without increasing power to the grid. From the simulation, it is found that SPS and CES can mitigate low-frequency oscillation on power system indicated by highest damping, smallest overshoot, and fastest settling time. It is also found that load demand can be increased significantly when SPS and CES installed to the system

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)