Gray wolf optimization‐based optimal grid connected solar photovoltaic system with enhanced power quality features

Chiu

et al. 2023

ELEKTRON ELEKTROTECH

In searching for a maximum power point (MPP) using a DC boost converter for photovoltaic (PV) energy conversion systems, we realised that the fast and accurate way to find the suitable duty ratio value is the core problem to enhance the energy conversion efficiency of the PV system. Under uniform irradiation, the panels will generate the same values, so they have only one peak on the P-V curve; conventional MPP tracking methods easily obtain this MPP. However, under partial shading conditions, many peaks are created, traditional MPP tracking methods can fall into the local MPP, and this issue will cause energy loss and reduce PV energy conversion efficiency. To avoid this disadvantage, this paper proposes a hybrid method (HM) by combining the improved chicken swarm optimisation (CSO) method and the incremental conductance (InC) algorithm for a DC standalone PV energy conversion system. In this hybrid method, the improved CSO modified approach is used to search the global region, and the InC algorithm is responsible for capturing the top of this global region. MATLAB simulation and experimental results were performed to demonstrate that the proposed method has achieved the global MPP under uniform solar irradiance and partial shadow effects.

“…The minimum and maximum values of the source impedance are calculated as in (7) and are seen in Fig. 2 with big green circles ,max ,max ,min ,max ,max ;,…”

Section: Usage Of DC Boost Converter In Mpp Trackingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New Approach-based MPP Tracking Design for Standalone PV Energy Conversion Systems

Chiu

et al. 2023

ELEKTRON ELEKTROTECH

“…Aside from the papers mentioned above, there are numerous studies in the specialized literature that employed master-slave methodologies to solve the problem of optimally integrating DGs into electrical networks [16][17][18]. Such methodologies share the same characteristics: (i) they are methodologies based on sequential programming that avoid the need for specialized software, (ii) they consider technical and financial aspects as their objective function and evaluate performance in terms of processing time and repeatability, and (iii) they require longer processing times to solve the problem under study.…”

Section: State-of-the-artmentioning

confidence: 99%

Integration of PV Distributed Generators into Electrical Networks for Investment and Energy Purchase Costs Reduction by Using a Discrete–Continuous Parallel PSO

et al. 2022

The problem of optimally integrating PV DGs into electrical networks to reduce annual costs (which include energy purchase and investment costs) was addressed in this research by presenting a new solution methodology. For such purpose, we used a Discrete–Continuous Parallel Particle Swarm Optimization method (DCPPSO), which considers both the discrete and continuous variables associated with the location and sizing of DGs in an electrical network and employs a parallel processing tool to reduce processing times. The optimization parameters of the proposed solution methodology were tuned using an external optimization algorithm. To validate the performance of DCPPSO, we employed the 33- and 69-bus test systems and compared it with five other solution methods: the BONMIN solver of the General Algebraic Modeling System (GAMS) and other four discrete–continuous methodologies that have been recently proposed. According to the findings, the DCPPSO produced the best results in terms of quality of the solution, processing time, and repeatability in electrical networks of any size, since it showed a better performance as the size of the electrical system increased.

“…The method has been efficaciously functional to many classical mathematical optimization problems as well as to improve WECS dynamic performance [27]. Similarly, grey wolf optimization (GWO) [28,29] is introduced that enables the avoidance of local optima, quicker implementation, and little parameter adjustment. Despite, it merits the aforesaid algorithms faces challenges like slower convergence, poor localization and provides solutions with poor accuracy.…”

Section: Introductionmentioning

confidence: 99%

Mitigation of power quality disturbances in wind energy conversion systems with fed unified power flow controllers using cascaded adaptive neuro-fuzzy inference system controller

2023

IJATEE

Power flow optimization is a critical aspect of power and control system design, aiming to minimize running costs while ensuring control variable tolerances and maximizing power delivery. The utilization of flexible alternating current transmission system (FACTS) particularly unified power flow controllers (UPFCs), enhances a system's power transfer capacity. UPFCs offer versatile adjustments for active and reactive power lines and voltage levels simultaneously, contributing to improved system performance and stability. A novel approach for enhancing flexibility and control in a doubly fed induction generator (DFIG)-based wind energy conversion system (WECS) was proposed. The technique involved the integration of a fuzzy-based control strategy for UPFC and the utilization of a cascaded adaptive neuro fuzzy inference system (ANFIS) to manage UPFC's series and shunt converters. Additionally, an improved butterfly optimization algorithm (IBOA) was introduced to ensure stability for DFIG-WECS. The methods were implemented and validated through simulations using MATLAB software. The effectiveness of the proposed approach was demonstrated through experimental results. The integration of cascaded ANFIS-based UPFC control and IBOA with WECS resulted in increased system power quality and stability. The total harmonic distortion (THD) of the system is reduced by 1.8%, indicating an enhancement in power quality. The application of UPFC and innovative control methodologies results in optimal power flow (OPF), maintaining stringent control variable tolerances in power systems. This research presents a systematic approach to enhancing flexibility and control in a DFIG-based WECS through the innovative use of UPFC and advanced control techniques. The proposed fuzzy-based UPFC control, in conjunction IBOA, significantly improves power quality and stability, thereby enhancing the overall efficiency of the system.