در حالی که تولید برق در جهان با فناوری‌های انرژی‌های تجدیدپذیر مانند توربین‌های بادی و انرژی گرمایی افزایش یافته است، انرژی خورشیدی به عنوان فراوان‌ترین منبع، انتخاب معقول و یکی از ارزان‌ترین اشکال منابع انرژی تجدیدپذیر شناخته می‌شود. در این مقاله به بررسی اجمالی انواع سیستم‌های خورشیدی پرداخته شده است.

مزارع بادی منابع پایان ناپذیر و تجدیدپذیر انرژی پاک هستند که نقش حیاتی در تغییر جهانی به سمت انرژی سبز و کاهش گازهای گلخانه‌ای دارند. بطور مرسوم مزارع بادی در خشکی و یا بصورت فراساحلی احداث شده که در این مقاله به مقایسه میان آن‌ها پرداخته شده است.

Properly sizing fuses for photovoltaic (PV) systems is critical for the safe, reliable and long-term operation of this renewable power source. Unlike typical electrical power distribution and control applications, fuses in photovoltaic systems are subject to unique conditions. Prolonged exposure to elements of the environment can produce abnormal ambient temperatures, which in turn affects fuse performance, conductor selection and sizing. Also, unlike traditional circuits which are normally sized based on continuous loads, PV modules produce continuous currents, leading to additional considerations when sizing fuses. Taking these conditions into account, a unique method for sizing fuses in PV systems is necessary. The following paper will first determine when fusing is required and secondly will outline a five-step process for sizing fuse ampere ratings

Power system stability is an important problem for power system operation. Determination of different stability margins can result in the optimum utilization of power system with minimum risk. Voltage stability is an important subset of power system stability. To correctly analyze the voltage stability of a power system, suitable dynamic models are usually required. However, static analysis tools can give us useful information about long term voltage stability. Especially, maximum loadability point (MLP) of a power system can be effectively estimated by modal analysis of load flow Jacobians. MLP is one of the important boundaries of voltage stability feasible region that loading beyond which is of little practical meaning. In this paper, MLP boundary of power system is analyzed by means of static analysis tools and its differences with the other boundaries of voltage stability, like saddle node bifurcation, are discussed. Effect of reactive power limits of generators and different static load models on the MLP border is also evaluated.

This paper presents a novel approach to estimate modal parameters of power systems for monitoring and analyzing the embedded modes of small signal oscillations. The proposed approach applies continuous wavelet transform (CWT) to identify damping and frequency of critical modes based on its time-frequency localization capability. The CWT has modified Morlet function as its mother wavelet. A procedure is also presented to fine-tune settings of the modified Morlet function of the CWT based on Heisenberg’s uncertainty principle and Shannon's entropy criterion. Additionally, high computational burden of the time-frequency methods is an important obstacle in online monitoring of power systems by these methods. To remedy this problem, the convolution integral of the CWT is calculated by efficient fast Fourier transform (FFT) routine in the proposed approach leading to a low computational burden. The proposed approach is compared with several other signal processing methods for modal identification of power systems. These comparisons illustrate effectiveness of the proposed approach, regarding run time, persistency against noise and estimation accuracy for online monitoring of small signal oscillations.

Evaluation of voltage stability status considering its dynamic boundaries is a key issue for saving global stability of power systems. However, this evaluation is a computationally demanding task and its implementation is very hard (if not impossible) for on-line environments such as dispatching centers of power systems. In this paper, a new viewpoint for the problem based on modeling it as a forecast process is proposed, which can be implemented with a low computation burden for practical power systems. For this purpose, a voltage stability classification model considering Hopf and limit induced bifurcations is proposed and a new forecast strategy to predict voltage stability class label based on the proposed classification is suggested. The suggested forecast strategy is composed of an information theoretic feature selection technique, extreme learning machine (ELM) as the forecast engine and a line search procedure to fine-tune the settings. The effectiveness of the proposed classification model and forecast strategy is extensively illustrated on the New England 39-bus and IEEE 145-bus test systems.

This paper presents a method of power quality classification using support vector machines (SVMs). In SVM training, the kernel parameters, and feature selection have very important roles for SVM classification accuracy. Therefore, most appropriates of these kernel types, kernel parameters and features should be used for the SVM training. In this paper to get optimal features for the classifier two stage of feature selection has been used. In first stage mutual information feature selection (MIFS) and in the second stage correlation feature selection (CFS) techniques are used for feature extraction from signals to build distinguished patterns for classifiers. MIFS can reduce the dimensionality of inputs, speed up the training of the network and get better performance and with CFS can get optimal features. In order to create training and testing vectors, different disturbance classes were simulated using parametric equations i.e., pure sinusoid, sag, swell, harmonic, outage, sag and harmonics and swell and harmonics. Finally, the investigation results of this novel approach are shown. The test results show that the classifier has an excellent performance on training speed, reliability and accuracy.

With continued increase in the electrical energy demand and tendency towards maximizing economic benefits in power transmission system, especially in the liberalized electricity markets, real-time voltage security analysis has become a growing concern in electric power utilities. However, static analysis methods, such as power flow-based methods, have difficulty in evaluating voltage stability and some voltage stability feasible region boundaries may not be correctly analyzed by these methods due to the use of simple models for the system components. On the other hand, dynamic modeling and evaluation of voltage stability are complex, expensive, and time consuming. In this paper, a new feature selection technique combined with a probabilistic neural network (PNN) is proposed for this purpose. A major difference of our paper with the previous research works in the area is that this paper proposes voltage stability prediction, while the previous works usually focus on the voltage stability evaluation. The proposed dynamic voltage stability prediction method is examined on the IEEE 14-bus and New England 39-bus test systems and the effectiveness of the proposed method is demonstrated. Also, the effect of different load models, branch contingencies, and generator contingencies is evaluated. Another advantage of the proposed prediction method is that it can be used for varied topologies and configurations of the power system.