مجله کیفیت و بهره وری صنعت برق ایران
سال دهم شماره 4 (پیاپی 25، زمستان 1400)

This paper proposes a new method for extracting dynamic properties for high impedance fault (HIF) detection using discrete Fourier transform (DFT). Unlike conventional methods that use features extracted from data windows after fault to detect high impedance fault, in the proposed method, using the disturbance detection algorithm in the network, the normalized changes of the selected features are used to compare the data windows after the fault occurrence and before the fault occurrence. It also uses the post-disturbance data windows, develops a decision system based on the output of the support vector machines (SVM) classifier, and compares the amount of certainty of other network events with high impedance fault. The reliability and security of the proposed method have been improved. The proposed method is implemented on an IEEE 34 bus network in EMTP-RV software. The simulation results show 97.2% accuracy, 98.5% reliability, and 98.8% security..

Automatic classification of power quality disturbances is the foundation to deal with the power quality problem. From a traditional viewpoint, the identification process of power quality disturbances should be divided into three independent stages: signal analysis, feature selection, and classification. However, there are some inherent defects in signal analysis and the procedure of manual feature selection is tedious and imprecise, leading to a low classification accuracy of multiple disturbances. To deal with these problems, this paper presents an automated system for the classification and identification of power quality disturbances. After receiving input signals, the proposed system requires some preprocessing such as changing the range of values by dividing the signals into their basic domains. In the next stage, the RMS value of the signal can be appraised to know the occurrence of the disturbance. If the RMS value of the input signal is not equal to the normal signal, the disturbance is occurring. To identify and classify disturbances, a novel deep learning-based method is developed. In this method, the activation function is expressed by a fuzzy approach. This makes the system more flexible. The benefits of the proposed strategy are separating the disturbances of basic frequency and using the nature of power quality signals as a tool for feature extraction. However, in the traditional method, for example, in empirical mode decomposition, the separation of signals from their components is not conveniently possible. To evaluate the proposed algorithm, a 33-bus distribution power network has been applied. The results reveal good agreement in comparison with other assessment tests.

In recent years, the issue of air pollution caused by greenhouse gas emissions and rising energy prices have contributed to developing and increasing the number of electric vehicles. Despite the many advantages of these vehicles, their increasing number and consequently their simultaneous charging in the distribution network can have destructive effects such as increased peak load, increased losses, unauthorized voltage drop, etc. On the other hand, managing the charging of vehicles by aggregators and using them as flexible loads and, if there is vehicle-to-grid (V2G) capability, as distributed generation units distributed across the distribution network can bring many financial and technical opportunities for the network. Accordingly, managing and planning the charging and discharging of these vehicles from the view point of network operators, aggregators, or vehicle owners in a centralized and decentralized manner are among the interesting topics that many articles have dealt with so far. This paper presents, a new solution for self-scheduling the charging and discharging of the private aggregator of electric vehicles to increase their profitability in the distribution network. Given the private ownership of the aggregator, it is obvious that the only factor influencing planning is cost reduction or profit enhancement, so its effect is unknown and/or negative on network indicators such as losses and voltage profiles. To solve this problem, a Time of Use (TOU) pricing model has been proposed by the Distribution Network Operator (DSO), so the aggregator plans to charge and discharge vehicles so that it can improve indicators such as losses and voltage profiles of the network in addition to be profitable. Density functions might have been used to include the uncertainty of vehicle drivers' behavior and to model the possible parameters related to him/her. Finally, the proposed approach is applied to a 33-bus test network by a genetic optimization algorithm using a private aggregator. The simulation results show that, in addition to maximizing the aggregator gain, the proposed method smoothes the network load curve, which reduces losses and improves voltage profile. It seems that in the probabilistic environment of vehicle behavior, the combination of TOU in private aggregator planning, which has led to an increase in their profits and at the same time in terms of the use of improved technical indicators, has not been studied yet.

New power systems based on virtual power plants (VPPs) will expand in the future, so it is of crucial importance for system operators to analyze the frequency behavior of these systems compared to current systems in the field of disruptions. In this paper, the equations of the frequency response model of conventional power systems are expanded by considering VPPs and their effective components, and a new model is presented for the frequency response of large power systems based on VPPs. To analyze the frequency behavior of the network, different scenarios are studied on a sample network. To cover the possible situations of the future network, several VPP models with different specifications are considered from the perspective of frequency responsiveness. The simulation results indicate the necessity of using frequency responsive devices in future power networks that are based on VPP.

To keep and improve the position of thermal power generation companies in the competitive market, performance evaluation is necessary. BSC is one of the most efficient performance evaluation models. This model has two assumptions of equal weights of perspectives and equal weights of performance evaluation indicators, which are not necessarily true in real-world problems. BWM is a strong method for weighting criteria in MADM problems. This research presents a trustable BWM algorithm for solving MADM problems with various indicators and hierarchical levels without considering the two mentioned assumptions. This algorithm can provide more trustable solutions for some real-world problems. Then, the weight of performance evaluation indicators of Fars Power Generation Management Company was evaluated by integrating the proposed algorithm with the BSC model. Seventeen performance evaluation indicators were identified in this company. Then, the identified indicators were classified and weighted in four perspectives using the BSC model. Based on the results, the integration of the proposed algorithm with the BSC model can be a suitable approach to assigning weights to indicators as per the performance evaluation of thermal power generation companies. Also, the algorithm is convergent and trustable and can provide reliable results for solving MADM problems according to various number of indicators and hierarchical level numbers.

This paper presents a framework for the optimal design of hybrid energy systems with a bat search optimization algorithm. Wind turbines, solar panels, and combined heat and power (CHP) systems are considered power generators. The proposed framework can exchange power with the upstream network, taking into account the limitations of power exchange, the cost of installing wind turbines, solar panels, CHP systems, battery storage, and converters in the form of investment costs plus maintenance costs, CHP system fuel costs, expected load removal costs, and power exchange costs as target functions. Also, a complete set of constraints related to the hybrid energy system is included in the problem framework. Uncertainties related to the production capacity of wind turbines and solar panels along with the uncertainty of consumer power demand using the two-point estimation method are also considered in the proposed framework. The bat search algorithm is used to find the optimal solution to the optimization problem. The results of the proposed framework in the form of different scenarios are compared with the results obtained by the genetic algorithm and particle swarm, which indicates its efficiency and optimal performance in designing hybrid energy systems.