This article presents an accurate computational technique for estimating the photovoltaic (PV) cell parameters from experimental measurements of the current-voltage (I-V) characteristics. The technique is based on using various evolutionary algorithms (EAs) and the double-diode eight-parameter cell model to precisely estimate unknown parameters. The proposed technique is implemented to extract the PV cell parameters of different manufacturer's modules by minimising the summation of absolute square errors between theoretical and measured I-V output characteristics obtained under different irradiation levels. The effectiveness and robustness of the proposed technique are demonstrated via a comparative assessment of the measured output I-V characteristics and those obtained by computer simulation, using Matlab SIMSCAPE library components. The good agreement obtained between theoretical and experimental results endorses the proposed approach to determine precisely the PV parameters required for power system studies. The proposed technique is useful power system studies with penetration of photovoltaic sources.

This paper presents a parameter estimation technique to determine the photovoltaic (PV) cell parameters from experimental measurements, using the direct search optimization method. A double diode model is considered to represent a photovoltaic (PV) cell, and a Matlab simulation is built to demonstrate the effects of varying various parameters on the output performance. The direct search optimization technique is used to estimate the cell parameters by reshaping the simulated output characteristics to match similar experimental measurements. The procedures that illustrate the implementation of the proposed technique to estimate the PV cell parameters are detailed. The technique is implemented to estimate the parameters of SUNSET PV Module Model PX-170 from experimental measurements. The convergence characteristics of the most sensitive cell parameters are given. The validity of the proposed technique is verified by comparing simulated results obtained using the estimated parameters and their corresponding measurements. The techniques and methodology presented in this paper are of prime importance to power system engineers forming a useful tool for studying power systems including photovoltaic sources.

This study proposes a new three-phase dual-rotor middle-stator brushless flux switching generator for 400 Hz dieseldriven aircraft ground power units. For the proposed machine, both field and armature windings are hosted in the stator in such a way that not only fulfils brushless structure, but also realises the flux switching function. Moreover, both windings have nonoverlapping concentrated windings to shorten the end-windings and reduce the copper losses. In the meantime, the rotor has only slots without any active parts. First, the machine detailed design is provided, and its performance is analysed using finite element method. Also, a selection topology of both stator and rotor pole arcs is carried out, targeting maximum generated electromotive force, minimum harmonic content, and minimum cogging torque. Then, a prototype is implemented and experimented to confirm the feasibility of the proposed machine.

Electrical power networks are expanded regularly to meet growing energy requirements. Reactive power dispatch (RPD) optimization is a powerful tool to enhance a system’s efficiency, reliability, and security. RPD optimization is classified as a non-linear and non-convex problem. In this paper, the RPD optimization problem is solved based on novel hybrid genetic algorithms—equilibrium optimizer (GAEO) optimization algorithms. The control variables are determined in such a way that optimizes RPD and minimizes power losses. The efficiency of the proposed optimization algorithms is compared to other techniques that have been used recently to solve the RPD problem. The proposed algorithm has been tested for optimization RPD for three test systems, IEEE14-bus, IEEE-30bus, and IEEE57-bus. The obtained results show the superiority of GAEO over other techniques for small test systems, IEEE14-bus and IEEE-30bus. GAEO shows good results for large system, IEEE 57-bus.

The integration of distributed generators (DGs) into distribution networks in optimal allocation is one of the main issues facing power system engineers to ensure improved stability and economic operation. This article presents a detailed analysis of the impacts of the optimal allocation and the number of DGs on both system steady-state and transient performances of distribution networks. An oscillatory particle swarm optimization (OPSO) algorithm was used to find the optimal allocations of DGs via minimizing various objective functions that deal with Total Transmission Losses, Voltage Regulation, and Power Performance Index. The OPSO is used to optimize these functions as a single and as a multiobjective optimization problem. The effectiveness of the method is demonstrated with the IEEE-14 bus as an example of distribution networks with a 50% increase in system loading. Two penetration scenarios have been considered, the optimal sizes and locations of DGs are obtained, and the results are presented.In addition, the impact of the penetration level of Photovoltaic and Wind Energy sources on transient performance is obtained using detailed nonlinear models of both synchronous machines and DGs sources. The system response is then obtained when the system is subjected to a three-phase short circuit fault for six cycles and the results are presented in a comparative form for different penetration levels. The techniques and results presented in the article form a useful base for power system engineers in planning and operating distribution systems with high penetration levels of RDG sources. K E Y W O R D Sconcentrated and distributed DG (C-DG, D-DG), distributed generation (DG), optimal DG allocations, oscillatory PSO, penetration level.