Journal of Operation and Automation in Power Engineering
Volume:11 Issue: 3, Autumn 2023

To assure the security and optimal protection coordination in meshed distribution networks, clearing faults swiftly and selectively is an essential priority. This priority, when hosting distributed generations (DGs), becomes the main challenge in order to avoid the unintentional DGs tripping. To overcome the mentioned challenge, this paper establishes a truth table to select new settings of directional overcurrent relays (DOCRs) characteristics which can be defined by users. It concentrates on minimizing the overall relays operating time. Typically, the conventional coordination between pair relays is achieved by two settings: time dial setting (TDS) and pickup current (Ip). Besides these adjustments, the suggested approach, considered the two coefficient constant of the inverse-time characteristics, namely the relay characteristics (A) and the inverse-time type (B), as continuous to optimize. Thus, more flexibility is attained in adjusting relays features. In addition, the inclusion of user-defined settings on numerical DOCRs, not only decreases the overall operating time of relays, but also improves the performance of the backup relays against the fault points. This approach illustrates a constrained non-linear programming model tackled by the combined particle swarm optimization (PSO) and whale optimization algorithm (WOA). The efficiency of the suggested approach is assessed though the IEEE 8-bus and the distribution portion of IEEE 30-bus system with synchronous-based DG units. The obtained results demonstrate the performance of approach and will be discussed in depth.

This paper reports support vector machine (SVM) based fault detection and classification in microgrid while considering distortions in voltages and currents, time and frequency series parameters, and differential parameters. For SVM-based fault classification, the data set is formed by analysing the operation of the standard IEC microgrid model, with and without grid interconnection, under different fault and non-fault scenarios. Fault scenarios also include different locations, resistances, and incident angles of fault. Whereas, for non-fault scenarios, the variation in load is considered. Voltages and currents from both ends of the distribution line (DL) are sampled at 1920 Hz. The time and frequency series parameters, total harmonic distortion (THD) in current and voltage, and differential parameters are determined. The SVM algorithm uses these parameters to detect and classify faults. The performance of this developed SVM based algorithm is compared with that of different machine learning algorithms. This comparative analysis reveals that SVM detects and classifies the faults on the microgrid with an accuracy of over 99.99%. The performance of the proposed method is also tested with 30 dB, 35 dB, and 40 dB noise in the generated data, which represent measurement errors.

The paper presents a Fast Fourier Transform (FFT) based Power Spectral Density (PSD) filter for denoising the PMU signal received from the smart power system network to identify the low frequency Oscillation modes (LFO). Small disturbances are introduced during normal operation of power system causes low frequency oscillations and may hinder the power system transfer capabilities of a system. The traditional signal processing method cannot extract the information from ambient signals effectively during noisy measurement. In this paper, the performance of the Prony analysis with reduced sampling rate is analysed for the PMU data with noiseless and noise environment. It is observed that, the performance of the Prony approach is not satisfactory under noisy measurement data.  In the present work FFT-PSD is used to denoise the noisy measurement signal and identify the nature of the decrement factor of the low frequency oscillatory modes. The accuracy of the estimated decrement factors of modes are verified with eigenvalues to validate the proposed method. The performance of proposed method is compared with signal processing method for IEEE New England power system and found effective and suitable during noisy PMU measurements.

With the development of electrical network infrastructure and the emergence of concepts such as demand response and using electric vehicles for purposes other than transportation, knowing the behavioral patterns of network technical specifications to manage electrical systems has become very important optimally. One of the critical parameters in the electrical system management is the distribution network imbalance. There are several ways to improve and control network imbalances. One of these ways is to detect the behavior of bus imbalance profiles in the network using data analysis. In the past, data analysis was performed for large environments such as states and countries. However, after the emergence of smart grids, behavioral study and recognition of these patterns in small-scale environments has found a fundamental and essential role in the deep management of these networks. One of the appropriate methods in identifying behavioral patterns is data mining. This paper uses the concepts of hierarchical and k-means clustering methods to identify the behavioral pattern of the imbalance index in an unbalanced distribution network. For this purpose, first, in an unbalanced network without the electric vehicle parking, the imbalance profile for all busses is estimated. Then, by applying the penetration coefficient of 25% and 75% for electric vehicles in the network, charging\discharging effects on the imbalance profile is determined. Then, by determining the target cluster and using demand response, the imbalance index is improved. This method reduces the number of busses competing in demand response programs. Next, using the concept of classification, a decision tree is constructed to minimize metering time.

Due to the presence of power electronic converters in electric vehicle battery chargers, the electrical power drawn from the distribution system has severe distortions which pose many problems to the power quality. Herein, the impact of chargers in terms of indicators, e.g., penetration level, battery state of charge, type of charging stations, the time of connection of chargers to the network, and the location of charging stations was comprehensively studied on a sample distribution network. The effect of these chargers was investigated based on power quality parameters, e.g., total harmonic distortion (THD) and voltage profile, and the effect of each indicator on these parameters was determined. To minimize the effects of the chargers, an IEEE 33-bus distribution sample network was optimized with the objective functions of voltage drop and THD. Based on this optimization algorithm, the installation placement and the power capacity of the charging stations were obtained to achieve the lowest voltage drop and THD.

The power systems operation has encountered some challenges due to the increasing penetration rate of renewable energy sources. One of the main challenges is the intermittency of these resources, which causes power balance violations. On the other hand, there are various distributed energy resources (DERs) to compensate for the need for the ramp capacity. Hence, to indicate this issue, the energy storage systems and the heating, ventilation, and air conditioning (HVAC) loads are selected in the form of a DER aggregator (DERA) to participate in the day-ahead (DA) energy and flexible ramping product (FRP) markets in this paper. Therefore, a co-optimization method is used to model the aggregator’s decision-making, as a mixed-integer linear programming (MILP) approach, in both the markets. The obtained results revealed that the profit of the DERA increases by considering not only its participation in the joint energy and FRP markets but also the potential of the HVAC loads. Moreover, the accuracy of the model is investigated using the sensitivity analysis of the parameters, including deployment probability, customers’ welfare, and the allowed temperature deviation.

This paper presents the output voltage control and execution of a novel non-isolated high step-up (NIHS) DC-DC converter connected to a solar photovoltaic (PV) based DC microgrid system. The proposed converter provides a high output voltage conversion ratio over smaller duty cycles, small inductors, low cost, and high efficiency to enhance the level of the generated voltages of PV. Also, to overcome the drawback of PV, the detailed operation of maximum power point tracking (MPPT) for the novel boost DC-DC converter topology is presented. A control algorithm, modified perturb and observe (MP&O), is put forward to assure that the maximum power is extracted from PV at any environmental condition. It regulates the output voltage of the PV system to the desired DC bus voltage. This technique is compared with the Incremental Conductance (INC) and conventional P&O algorithm in terms of their computational complexity and oscillations near maximum power point (MPP) using MATLAB & Simulink. The focus is on the continuous conduction mode of the proposed converter. To demonstrate the effectiveness of the proposed converter, operation modes, and technical analysis are conducted. Also, the experimental results of a 200 W-12V/120V, 25 kHz prototype are given and discussed to justify the suggested converter.

Increasing requirements of electric vehicles with different capacities of batteries and increasing number of small-sized renewable energy sources lead to complexity of calculations, voltage drop, power quality loss, and unevenness in the load curve. This paper proposes a modified version of the mean-field decentralized method to smooth the load curve, maximize vehicle owners' profit, and meet vehicle owners’ demands. Different capacity of batteries is a challenging problem in the charging and discharging control of electric vehicles; so to solve this problem, a weighted average method is used, which determines the design weighting parameters based on the capacity of batteries. Finally, a comparison has been made between five different centralized and decentralized strategies with weighted and weightless average methods.