Voltage Stability Assessment of Radial Distribution Systems Including Optimal Allocation of Distributed Generators

Assessment of power systems voltage stability is considered an important assignment for the operation and planning of power system. In this paper, a voltage stability study using Continuous Power Flow (CPF) is introduced to evaluate the impact of Distribution Generator (DG) on radial distribution systems. On the way to allocate the DG, a hybrid between the Voltage Stability Index (VSI) and Whale Optimization Algorithm (WOA) is developed. The main purpose of using VSI is to find the most sensitive buses for allocating the DG in the system. Hence, Fuzzy logic control with the Normalized VSI (NVSI) and the voltage magnitude at each bus are used to determine the candidate buses. However, the best DG size is calculated using WOA. Four standard radial distribution systems are used in this paper; 12, 33, 69, and 85-bus. The developed hybrid optimization method is compared with other existing analytical and metaheuristic optimization techniques to prove its efficiency. The results prove the ability of the developed method in the allocation of DG. In addition, the influence of the DG integration on enhancing the voltage stability through injecting the proper active and reactive powers is studied

Improvement of dielectric strength and properties of cross-linked polyethylene using nano filler

Power cables insulated with cross-linked polyethylene (XLPE) have been utilized worldwide for distribution and transmission networks. There are several advantages for this type of insulation; it has better electrical, thermal, and mechanical properties compared to other types of insulation in medium and high voltage networks. Many studies aimed to improve the XLPE characteristics through introducing nano fillers to the XLPE matrix. Therefore, this paper investigates the AC (HV) breakdown voltage (dielectric strength) of XLPE after adding nano-sized zeolite (Z) fillers with various concentrations of 1 wt%, 3 wt%, 5 wt% and 7 wt%. The dielectric strength is tested in different temperatures of 30 ⁰C and 250 ⁰C. Additionally, it was tested in low and high salty wet conditions. The dielectric strength of the XLPE has been enhanced by inducing the Z nano filler. The results of the tests were used to train the artificial neural network (ANN) to calculate the dielectric strength of XLPE composites with different concentrations of nano Z filler under different weathering conditions. Thermogravimetric analysis, tensile strength, and elongation at break tests were applied to check the thermal and mechanical characteristics of the samples. Experimental findings show that the optimum concentration of nano Z is 3.64 wt% to enhance the electrical, thermal, and mechanical properties

Carbon Nanotubes Effect for Polymer Materials on Break Down Voltage

Epoxy resin composites reinforced to different types of carbon nano-particles have been fabricated. Carbon black (20, 30 and 40 wt. %), graphene (0.5 to 4 wt. %) and carbon nanotubes (CNT) (0.5 to 2 wt. %) were added with different weight percentages to epoxy. The dielectric strength of composites was tested in several conditions such as (dry, wet, low salinity and high salinity). The mechanical characterization showed that the nano-composite Polymer enhanced by using these particles in the tensile strength. Thermal gravimetric analysis shows effect of these nano-particles on the thermal structure of epoxy resin. Scanning Electron Microscopic test is used to characterize the dispersion of carbon nano-particles and to analysis the fractured parts in the nano scale

Performance Enhancement of Wind Farms Using Tuned SSSC Based on Artificial Neural Network

Recently, power systems are confronting a lot of challenges. Increasing the dependence on renewable energy sources especially wind energy and its impact on the stability of electrical systems are the most important challenges. Flexible alternating current transmission systems (FACTS) can be used to improve the relationship between wind farms and electrical grids. The performance of these FACTS depends on the parameters of its control system. These parameters can be tuned using modern methods like Artificial Neural Network (ANN). In this paper, ANN is used to improve the performance of static synchronous series compensator (SSSC) integrated into combined wind farm (CWF). This CWF is composed of squirrel cage induction generators (SCIG) and doubly fed induction generators (DFIG) wind turbines. This wind farm is collecting the advantage of SCIG and DFIG wind turbines. To view out the motivation of this paper, a comparison is done among the performances of combined wind farm (CWF) with ANN-SSSC, CWF with ordinary SSSC and CWF with SSSC tune by Multi-objective genetic algorithm (MOGA SSSC). The root mean square Error (RMSE) is used to evaluate the results. The results illustrate that the performance of CWF can be improved using SSSC adjusted by ANN

Optimal allocation of a wind turbine and battery energy storage systems in distribution networks based on the modified BES-optimizer

Recently, incorporating renewable energy resources (RERs) like wind turbines (WTs) in a distribution network is rapidly increased to meet the load growth. However, distribution networks have been facing many challenges to withstand the intermittent output power of RERs. Battery energy storage (BES) is used with RERs to smoothly inject the output power to the grid by RERs. Therefore, this paper proposes an effective strategy for optimal allocation of WT and BES in RDS to decrease the total system losses. In addition, a modified bald eagle search (BES-optimizer) is proposed to obtain the preferable allocations of WT and BES simultaneously in the radial distribution system (RDS) considering the probabilistic distribution of the WT and load demand. IEEE 69-bus RDS is utilized as a test system. Based on the obtained results, installing WTs with BES gives better results than installing WTs alone in the RDS. However, the proposed algorithm proved its efficiency to obtain the best global results compared with other well-known techniques

Aging Effect on Characteristics of Oil Impregnated Insulation Paper for Power Transformers

This paper deals with experimental investigations of the electrical and physical properties of oil impregnated insulation paper for power transformers at different temperatures. The ac breakdown voltage, tensile strength and water content of insulation papers impregnated in mineral oil for different time periods were investigated. The effect of insulation paper thickness on the electrical and mechanical properties has also been studied. The results showed that the breakdown voltage and the tensile strength decreased with increasing the time of immersion of insulation paper in oil at room tem-perature, at 5 and at −12◦C. Also, the thermal aging effect on the characteristic of insulation paper has been studied. It was found that high temperatures affect the breakdown voltage and the tensile strength to a great extent

Machine Learning Technique to Predict Flashover Voltage of Nanocomposite Materials

Epoxy resin is frequently employed in medium- and high- voltage transmission insulation, because of its low dielectric losses and excellent temperature resistance. At low temperatures, epoxy resin has high chemical resistance. Epoxy resin insulated types have largely superseded the conventional, paper-insulated varieties in numerous cable sectors owing to several advantages. Numerous studies have been conducted to enhance the properties of epoxy resin. With the addition of silicon dioxide (SiO2) nanofiller, the electrical and physical properties of epoxy resin are intended to be improved in this research. Epoxy resin composites with SiO2 filler were created with lengths of 5, 10, 15, and 20 mm and concentrated at 7wt%. Subsequently the key findings of this research are outlined, highlighting the significance of this study's focus on polymer utilized in the highly competitive and technologically advanced power industry, which is used globally

Incorporating photovoltaic inverter capability into stochastic optimal reactive power dispatch through an enhanced artificial gorilla troops optimizer

Abstract This study addresses the challenging task of solving optimal reactive power dispatch (ORPD) while incorporating renewable energy resources (RERs), considering their stochastic and time‐varying nature. Specifically, the focus is on solving the stochastic optimal reactive power dispatch (SORPD) problem, taking into account uncertainties in load demand and generated power, as well as the reactive power generation capability of photovoltaic (PV) systems. To tackle this problem, an enhanced Artificial Gorilla Troops Optimizer (EGTO) is proposed, which utilizes multiple strategies. The objective is to minimize power loss and improve voltage profile and system stability under uncertain conditions. The algorithm is applied and tested on the IEEE 30‐bus system, both with and without the STATCOM functionality of the PV system. A comparison is conducted against other well‐known optimization algorithms. The results demonstrate the significant improvement achieved by incorporating the PV unit. The inclusion of the PV system leads to reduced expected power losses, voltage deviations, and improved voltage stability. Specifically, without and with the STATCOM functionality, the expected power loss decreases from 5.9218 MW at the base case to 1.1419 MW and 1.1197 MW, respectively. Similarly, the expected voltage deviation decreases from 1.9320 p.u. to 0.0909 p.u. and 0.0893 p.u., respectively, and the expected voltage stability decreases from 0.1336 p.u. to 0.07199 p.u. and 0.07142 p.u., respectively