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This paper presents an effective biogeography-based optimization (BBO) for optimal location and sizing of solar photovoltaic distributed generation (PVDG) units to reduce power losses while maintaining voltage profile and voltage harmonic distortion at the limits. This applied algorithm was motivated by biogeography, that the study of the distribution of biological species through time and space. This technique is able to expand the searching space and retain good solution group at each generation. Therefore, the applied method can significantly improve performance. The effectiveness of the applied algorithm is validated by testing it on IEEE 33-bus and IEEE 69-bus radial distribution systems. The obtained results are compared with the genetic algorithm (GA), the particle swarm optimization algorithm (PSO) and the artificial bee colony algorithm (ABC). As a result, the applied algorithm offers better solution quality and accuracy with faster convergence.

In the past, wind power penetration was extremely limited compared to the total power production. As a result, the interconnection requirements for wind farms were not included in the grid codes. However, in the recent years, the significant amount of energy injected by wind farms has already impacted the power system, both from a technical and a regulatory point of view in the recent years. Large wind plants have a significant influence on power system operation since they are related to unpredictability of the primary source. Thus wind turbines must improve their quality production to ensure the stability and reliability of the power system as conventional power plants. Wind energy is not constant and, since wind turbines output is proportional to the cube of wind speed, this causes the power output of Squirrel-Cage Induction Generator Wind Turbine (SCIG WT) to fluctuate. In order to improve power quality and maintain the stable output generated from SCIG wind farm, this paper presents a hybrid controller based on PI and fuzzy technique for the pitch angle controller which has been one of the most common methods for smoothing output power fluctuations. All models as well as controllers here presented are developed using Matlab-Simulink software

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Abstract:Under power system short-circuits, the Doubly-Fed Induction Generator (DFIG) Wind Turbines (WT) are required to be equipped with crowbar protections to preserve the lifetime of power electronics devices. When the crowbar is switched on, the rotor windings are short-circuited. In this case, the DFIG behaves like a squirrel-cage induction generator (SCIG) and can adsorb reactive power, which can affect the power system. A DFIG based-fault-ride through (FRT) scheme with crowbar, rotor-side and grid-side converters has recently been proposed for improving the transient stability: in particular, a hybrid cascade Fuzzy-PI-based controlling technique has been demonstrated to be able to control the Insulated Gate Bipolar Transistor (IGBT) based frequency converter in order to enhance the transient stability. The performance of this hybrid control scheme is analyzed here and compared to other techniques, under a three-phase fault condition on a single machine connected to the grid. In particular, the transient operation of the system is investigated by comparing the performance of the hybrid system with conventional proportional-integral and fuzzy logic controller, respectively. The system validation is carried out in Simulink, confirming the effectiveness of the coordinated advanced fuzzy logic control.

A common problem in wind power plants involves fixed-speed wind turbines. In fact, being equipped with a squirrel-cage induction generator (SCIG), they tend to drain a relevant amount of reactive power from the grid, potentially causing voltage drops and possible voltage instability. To improve SCIG power quality and transient stability, this paper investigates a new control strategy for pitch angle control based on proportional-integral (PI) controller and a fuzzy logic controller (FLC), considering both normal and fault ride-through (FRT) schemes. In the literature, often, the mechanical torque output is assumed constant for a specific wind speed. This might not be accurate, because the mechanical torque-speed typical of a wind turbine depends also on the power coefficient or pitch angle. In this paper, an analytic model of transient stability is proposed using the equivalent circuit of the SCIG and using the concepts of stable and unstable electrical-mechanical equilibrium. The method has been evaluated by comparing the results obtained by the analytic method with the dynamic simulation. The results show that the proposed hybrid controller is effective at smoothing the output power and complying with FRT requirements for SCIG in the power system.

In this paper, stochastic fractal search method (SFS) is employed for solving the optimal reactive power flow (ORPF) problem with a target of optimizing total active power losses (TPL), voltage deviation (VD), and voltage stability index (VSI). SFS is an effective metaheuristic algorithm consisting of diffusion process and two update processes. ORPF is a complex problem giving challenges to applied algorithms by taking into account many complex constraints such as operating voltage from generators and loads, active and reactive power generation of generators, limit of capacitors, apparent power limit from branches, and tap setting of transformers. For verifying the performance, solutions of IEEE 30 and 118-bus system with TPL, VD, and VSI objectives are found by the SFS method with different control parameter settings. Result comparisons indicate that SFS is more favorable than other methods about finding effective solutions and having faster speed. As a result, it is suggested that SFS should be used for ORPF problem, and modifications performed on SFS are encouraged for better results.

This paper proposes an efficient and new modified differential evolution algorithm (ENMDE) for solving two short-term hydrothermal scheduling (STHTS) problems. The first is to take the available water constraint into account, and the second is to consider the reservoir volume constraints. The proposed method in this paper is a new, improved version of the conventional differential evolution (CDE) method to enhance solution quality and shorten the maximum number of iterations based on two new modifications. The first focuses on a self-tuned mutation operation to open the local search zone based on the evaluation of the quality of the solution, while the second focuses on a leading group selection technique to keep a set of dominant solutions. The contribution of each modification to the superiority of the proposed method over CDE is also investigated by implementing CDE with the self-tuned mutation (STMDE), CDE with the leading group selection technique (LGSDE), and CDE with the two modifications. In addition, particle swarm optimization (PSO), the bat algorithm (BA), and the flower pollination algorithm (FPA) methods are also implemented through four study cases for the first problem, and two study cases for the second problem. Through extensive numerical study cases, the effectiveness of the proposed approach is confirmed.