To modernize electrical power systems on isolated islands, countries around the world have increased their interest in combining green energy with conventional power plants. Wind energy (WE) is the most adopted renewable energy source due to its technical readiness, competitive cost, and environmentally friendly characteristics. Despite this, a high penetration of WE in conventional power systems could affect their stability. Moreover, these isolated island power systems face frequency deviation issues when operating in hybrid generation mode. Generally, under contingency or transient conditions for hybrid isolated wind–diesel power systems (WDPSs), it is only the diesel generator that provides inertial support in frequency regulation (FR) because wind turbines are unable to provide inertia themselves. Frequency deviations can exceed the pre-defined grid code limits during severe windy conditions because the diesel generator’s inertial support is not always sufficient. To overcome this issue, we propose a control strategy named emulation inertial and proportional (EI&P) control for Variable-Speed Wind Turbines (VSWTs). VSWTs can also contribute to FR by releasing synthetic inertia during uncertainties. In addition, to enhance the effectiveness and smoothness of the blade pitch angle control of WTs, a pitch compensation (PC) control loop is proposed in this paper. The aim of this study was to provide optimal primary frequency regulations to hybrid wind–diesel power systems (WDPSs). Therefore, the hybrid WDPS on San Cristobal Island was considered in this study. To achieve such goals, we used the above-mentioned proposed controls (EI&P and PC) and optimally tuned them using the Student-Psychology-Based Algorithm (SPBA). The effectiveness of this algorithm is in its ability to provide the best optimum controller gain combinations of the proposed control loops. As a result, the FD in the WDPS on San Cristobal Island was reduced by 1.05 Hz, and other quality indices, such as the integral absolute error (IAE), integral square error (ISE), and controller quality index (Z), were improved by 159.65, 16.75, and 83.80%, respectively. Moreover, the proposed PC control, which was further simplified using exhaustive searches, resulted in a reduction in blade pitch angle control complexity. To validate the results, the proposed approach was tested under different sets of perturbations (sudden loss of wind generator and gradual increase in wind speed and their random behavior). Furthermore, hybrid systems were tested simultaneously under different real-world scenarios, like various sets of load or power imbalances, wind variations, and their combinations. The Simulink results showed a significant improvement in FR support by minimizing frequency deviations during transients.

In recent decades, renewable energy sources, such as wind power, have extraordinarily increased their participation in the energy mix throughout the world. This progression has played an important role in lowering the usage of fossil fuels. In addition, it has reduced environmental hazards and increased the emergence of hybrid power systems, mainly in remote areas. In some of these areas, diesel power plants were the only previous source of energy. Irrespective of the benefits, hybrid power systems might face problems such as frequency deviations. To contribute to reducing these problems, this paper presents a methodology to tune diesel engine governors using the Student Psychology-Based Algorithm. This proposed methodology enhances some metrics of controller performance, such as the integral square error, integral absolute error, and number of sign changes in the frequency derivative. This approach has been tested against different perturbations (step, ramp and random). To validate the effectiveness of the proposed approach, it has been simulated in relation to the San Cristobal Island (Ecuador) hybrid wind–diesel power system. The simulation results show that the governor tuned with the proposed approach provides a better system response.

Increased renewable energy penetration into conventional power plants results in significant frequency regulation (FR) problems, particularly at island power systems. To overcome FR problems, an energy storage system plays an important role. Therefore, a battery energy storage system (BESS) is proposed to provide primary frequency control (PFC) for San Cristobal Island hybrid wind diesel power system (WDPS) in this paper. The aim of this paper is to highlight the improvements achieved using BESS and estimate the optimal size of that BESS for San Cristobal WDPS. Therefore, a permanent magnet synchronous generator (PMSG) based variable speed wind turbine (VSWT) with synthetic (droop) control is used, which enables VSWT to release inertia during contingencies. A novel controller tuning methodology, named student psychology-based algorithm (SPBA), is used to optimally tune the BESS with hybrid WDPS and to estimate BESS size. In addition, several configurations set such as BESS with VSWT and/or diesel power plant (DPP) or both, are presented in this paper to highlight the optimal tuning effects. Moreover, the BESS based WDPS is tested under various real-world scenarios such as loss of wind generator, steadily increase wind speed (ΔV_w≈ 0.81 ms^-1), variable wind speed, variable load demand (P_load) and simultaneously change in V_w and P_load. Finally, hybrid BESS based WDPS is tested in a real time environment (OPAL-RT) which validates its performance. Results show the significant reduction in frequency deviation, optimal sizing of BESS and optimal tuning of the whole WDPS.

Incremental energy demand, environmental constraints, restrictions in the availability of energy resources, economic conditions, and political impact prompt the power sector toward deregulation. In addition to these impediments, electric power competition for power quality, reliability, availability, and cost forces utilities to maximize utilization of the existing infrastructure by flowing power on transmission lines near to their thermal limits. All these factors introduce problems related to power network stability, reliability, quality, congestion management, and security in restructured power systems. To overcome these problems, power-electronics-based FACTS devices are one of the beneficial solutions at present. In this review paper, the significant role of FACTS devices in restructured power networks and their technical benefits against various power system problems such as load frequency control, voltage stability, and congestion management will be presented. In addition, an extensive discussion about the comparison between different FACTS devices (series, shunt, and their combination) and comparison between various optimization techniques (classical, analytical, hybrid, and meta-heuristics) that support FACTS devices to achieve their respective benefits is presented in this paper. Generally, it is concluded that third-generation FACTS controllers are more popular to mitigate various power system problems (i.e., load frequency control, voltage stability, and congestion management). Moreover, a combination of multiple FACTS devices, with or without energy storage devices, is more beneficial compared to their individual usage. However, this is not commonly adopted in small power systems due to high installation or maintenance costs. Therefore, there is a trade-off between the selection and cost of FACTS devices to minimize the power system problems. Likewise, meta-heuristics and hybrid optimization techniques are commonly adopted to optimize FACTS devices due to their fast convergence, robustness, higher accuracy, and flexibility.

This paper is devoted to develop interest of power system engineers in learning basic concepts of image processing and consequently using deep networks to solve problems of complex power system networks. To this end, we study fault classification in a power system through automation of equal area (EAC) criterion. By considering EAC graphs as images and using classical image processing techniques, we successfully distinguish between different transient conditions including sudden change of input power as well as short circuit at the sending end and middle points of a single and double circuit transmission lines. In addition to classification, some parameters are also determined from EAC images such as initial rotor angle, clearing angle, and maximum rotor angle. Further, the use of deep networks is introduced to perform the same task of fault classification and a comparison is drawn with multilayer perceptron neural networks. Developed algorithms are tested in MATLAB as well as Pytorch environments.

Due to adaptation of new lifestyles, energy demand in the power sector rapidly increased day-by-day. To overcome this energy gap Flexible Alternating Current Transmission System (FACTS) controllers are the most reliable option irrespective to upgradation of power networks. Static Synchronous Series Compensator (SSSC) is one of a series family, second generation FACTS controller used in power networks to enhance power transfer capacity. In this paper, a new SSSC based indirect controller is proposed for a 500kV grid station. In SSSC, multilevel, 48pulse, Neutral Point Clamped (NPC) based Voltage Source Converter (VSC) is implemented that injects a controllable compensating voltage in series with transmission line. Almost 20% enhanced power flow can be achieved by using proposed cost-effective SSSC controller. The aim of this paper is to find drawbacks associated with previously designed controller and design a new controller for power flow improvement. Also compare their results for power flow in 500kV grid station using MATLAB/Simulink 2017 (a). . . . . .