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Load flow analysis is an essential part of system operation and planning. Up to now, load flow can be categorized as deterministic and probabilistic. In the deterministic load flow, the generations and loads are fixed. However, the state variables of power systems have uncertain nature due to the random load variations and stochastic distributed generations. In this regard, the probabilistic load flow (PLF) is implemented. The inputs of PLF are probability density functions (PDFs) of buses powers and the outputs are PDFs of system states. Therefore, the relation between the system variables over the time will be lost. To overcome this issue, analytic time series load flow (analytic TLF) is introduced in this work. TLF considers the effect of time synchronization and correlation between different loads in the network. Auto regressive moving average (ARMA) models are used to model the time varying loads and generations. The inputs in analytic TLF are ARMA model parameters of active and reactive powers of load buses and the active power of generation buses. The outputs are ARMA parameters of the voltage magnitude and phase of load buses as well as voltage phase of generation buses. The performance of the proposed analytic TLF is evaluated using several examples. [ABSTRACT FROM AUTHOR]

Due to increase in penetration of Distributed Generations (DGs) in power systems, fault current level is being increased, which results in some problems in the systems. Fault Current Limiters (FCLs) are attractive devices to tackle these problems for transmission and distribution systems. The utilized FCLs may have considerable impact on the signals used for differential protection of power transformers, which leads to mal-operation of these protections. It seems a comprehensive analysis is necessary for performance evaluation of differential protection algorithms in presence of FCLs. This paper deals with investigation of FCLs impact on power transformers’ differential protection. The performance of some well-known differential protection algorithms for discrimination between internal fault current and magnetizing inrush current with and without presence of FCL are evaluated. [ABSTRACT FROM AUTHOR]

Stand-alone micro-grids (MGs) and grid-connected MGs with high penetration level of Distributed Generation (DG) are growing at a fast rate. In these grids, the power quality disturbances such as harmonics, inter-harmonics and deviation from the fundamental frequency are widespread. The influence of these disturbances on two categories of digital metering algorithms is evaluated in this paper. The first category is related to the measurement of the fundamental frequency component of current and voltage signals, which are used as inputs of the digital protection algorithms. Most of the relays only use fundamental frequency component of their input voltage and current signals to fulfill the desired protective functions. The accuracy of the protective relays highly depends on the performance of the applied algorithms for extracting the fundamental components. The second evaluated category is related to the reactive power calculation algorithms, which are used in the static VAr compensator (SVC) control system. Utilizing SVC is growing due to the DGs such as wind turbines magnify voltage fluctuations called flicker. Since the SVC compensates only the reactive power related to the fundamental frequency component, the reactive power signal should not be sensitive to the harmonics. In addition, the reactive power calculation should be fast enough that SVC can follow the abrupt changes to mitigate the flicker, efficiently. In this paper, frequency deviation, harmonic, and inter-harmonic phenomena are considered as the most causes for power quality problems. Their influence in digital metering algorithms is evaluated. It is confirmed that defining new indices is mandatory to fully reflect algorithms׳ accuracy and efficiency. Furthermore, the field measurements of instantaneous voltage and current of a wind farm and electric arc furnaces are used for the evaluation. [ABSTRACT FROM AUTHOR]

Islanding is one of the most important concerns of the grid connected distributed resources due to personnel and equipment safety. Many approaches have been proposed for islanding detection, which can be categorized into passive and active schemes. The main concern of the passive schemes is related to their large Non Detection Zone (NDZ), while the main problem of the active methods is related to their negative impact on power quality. This paper propose an efficient and intelligent islanding detection algorithm using combination of an optimal Artificial Neural Network (ANN) based on Particle Swarm Optimization (PSO) with a simple active method. The intelligent islanding detection method based on ANN, may have mal-detection in the case of change in the power network structure. In the proposed scheme, ANN is adapted with change in power network structure to reduce NDZ. Optimal parameters of the ANN such as weight coefficients and biases are derived using the PSO in order to minimize the technique NDZ. Also the performance of the various structures of ANN such as Multilayer Perceptron (MLP), Radial Basis Function (RBF) and Probabilistic Neural Network (PNN) in combination with PSO is compared for islanding detection purpose. The proposed method is simulated and tested in various operation conditions such as islanding conditions, motor starting, capacitor bank switching and nonlinear load switching. The test results showed that it correctly detects the islanding operation and does not mal-operate in the other situations and has a small NDZ. [ABSTRACT FROM AUTHOR]

Linear models are capable of capturing the Linear Deterministic (LD) component of the time series. In order to benefit from both Nonlinear Deterministic (ND) and LD components during the prediction procedure, it is necessary to employ nonlinear models. The complexity of the prediction algorithm increases when nonlinear models are utilized. Hence, before applying nonlinear models the presence of nonlinear component should be confirmed. Although surrogate data technique uses various tests to indicate the nonlinearity, in many cases its test results are different and in conflict with each other. The reason is time series include LD and ND components together and giving a strict answer about nonlinearity cannot be applicable. Here instead of such a strict answer, by quantizing the ND component, a new index (a number between 0 and 1) is proposed (the closer to 1 the more ND components). In this method first we use ARMA models. The residual series is used to calculate the proposed index which it contains all components of the original series except LD. The proposed procedure is applied to three different case studies. Furthermore, the performance of some nonlinear prediction methods (Markov, Grey, Grey–Markov, EMD–Grey, NARnet and ARMAX) is compared with the proposed index. [ABSTRACT FROM AUTHOR]