In recent years, due to the fact that non-renewable energy is coming to an end, renewable energy is expected to provide a significant part of the future needs of Iran. Among these energies, solar energy can be used in a wider range of country, due to availability, proper radiation intensity, high sunshine during the year (about 300 sunny days per year), Installation in the desired power and a lot of land in many parts of the country will have a significant share. The purpose of this paper is to analyze the performance of 1 MW Arak solar power plant (Iran’ s first megawatt power plant) according to IEC-61724 standard using data recorded over a year (November 2016 to November 2017). The Arak Solar Power Plant was built at latitude of 34° 3’ , 2” north, and longitude 49° 47’ 47” at an altitude of 1700 meters above sea level. The plant has the capacity of 1 MW in a 1. 6-acre land with 3920 modules (1920*260 Watt Monocrystals and 2000*250 Watt Polycrystals) in 200 structures and four inverters (each with a capacity of 250 kW). The information of this power plant is recorded in fifteen-minute intervals, which according to IEC-61724, this power plant is in the class B and we can do one-year performance analysis. In this paper, the performance parameters of the system are presented, also a table for comparison with other power plants in some parts of the world has been created.

In this paper, a novel approach is introduced for Fault Detection and Fault Location in power systems that incorporate Large-Scale Photovoltaic Power Plants (LSPPPs). Given that short-circuit (SC) characteristics in photovoltaic systems differ significantly from those observed in traditional Synchronous Generators (SGs). The increasing integration of LSPPPs into the power grid is anticipated to have an impact on the performance of conventional protection relay systems; initially designed for SG-dominated setups. Therefore, the proposed method revolves around analyzing the influence of LSPPPs on the alteration of observed transmission line impedance to identify and locate faults accurately. Furthermore, the methodology takes into consideration factors such as fault location, fault resistance, fault type, changing the LSPPP generation, and noise conditions. when calculating the phase angle of the fault loop current. The effectiveness of this approach was assessed through testing and evaluation on 2-bus and IEEE 39-bus test systems connected to an LSPPP, simulated using PSCAD/EMTDC and MATLAB/SIMULINK.

Recent advancements in photovoltaic (PV) system technologies have decreased their investment cost and enabled the construction of large PV farms for bulk power generations. The output power of PV farms is aected by both failure of composed components and solar radiation variability. These two factors cause the output power of PV farms be random and dierent from that of conventional units. Therefore, suitable models and methods should be developed to assess dierent aspects of PV farms integration into power systems, particularly from the system reliability viewpoint. In this context a reliability model has been developed for PV farms with considering both the uncertainties associated with solar radiation and components outages. The proposed model represents a PV farm by a multi-state generating unit which is suitable for the generation system assessment. Utilizing the developed reliability model, an analytical method is proposed for adequacy assessment of power generation systems including large PV farms. Real solar radiation data is used from Jask region in Iran which are utilized in the studies performed on the RBTS and the IEEE-RTS. Several dierent analyses are conducted to analyze the reliability impacts of PV farms integration and to estimate the capacity value of large PV farms in power generation systems.

Iran is not rich in forest land and only 7.5 per cent of country is covered by forest. Therefore Iran has to import wood based panel annually. It is estimated that 100.000.000 Date Palms trees covered an area at about 770.000 hectares around world, and Iran has got 220.000 ha. Because of several reasons Date palm trees should be pruned and each tree produce 10-20 kg residues annually. In this study, for making boards Date Palm pruning residues as raw material and employed UF resin as the binder. Steaming time (5 and 8 Min.), steaming temperature (135 and 145oC) and press time (1, 2 and 3 Min.) were used as variable factors and three boards were made in Pilot plant scale for each combination of variables. Samples of all made boards were tested by the methods of EN to obtain mean values of bending strength, and internal bond strength (I.B). In addition samples of each type of board were tested for thickness swelling after soaking in water for 2 hours and 24 hours respectively.The results of this study show clearly that boards have higher mechanical properties than the MDF property requirements which are recommended by EN standard.

Till now the conversion efficiency of the commercial photovoltaic (PV) solar modules is in the range of 14 to 20%. Therefore, PV power plants need very large area to achieve the desired output power. This paper presents some proper calculations to estimate land area occupied by the PV array. Calculations for the minimum and the maximum land area for a range of PV array with power capacity from 1 to 250 kW for different latitudes in the northern hemisphere were presented. Six different PV modules were selected for the calculation. A group of correlations are presented to roughly estimate both the land area and the number of PV modules per row as a function of the total power capacity required. In addition, correlations suggested to calculate the spacing between the rows in the PV array as a function of local latitude.

Nowadays, the rapidly growing energy consumption in the world along with the shortage of ‎‎fossil ‎‎fuels and their high ‎environmental pollution has led to increased attention to renewable ‎‎energies such ‎as solar, wind, and geothermal ‎energies. ‎Among these, solar energy has many ‎advantages such as no ‎ecological and ‎noise ‎pollution and free use. ‎However, photovoltaic power ‎plant output, due to its ‎dependency on solar irradiance and other weather ‎‎conditions, has ‎‎uncontrollable uncertainty. ‎Therefore, for providing high-quality electric energy for ‎end-consumers and enhancing the ‎reliability of ‎the system, photovoltaic power output needs to be predicted ‎‎accurately. The aim of this paper is to ‎‎address this issue ‎by proposing three short-term photovoltaic power-forecasting models based on ‎deep-learning neural ‎‎networks, which differ in terms of input types and network structures. The proposed ‎models use ‎long short-term ‎memory (LSTM) in their structures and historical power outputs and ‎weather conditions as their ‎inputs to forecast one-‎hour-ahead PV power. ‎Conducted experiments show ‎that employing weather ‎conditions, in addition to ‎the historical ‎‎output powers, increases the prediction ‎accuracy. Moreover, ‎utilizing more complicated ‎network ‎structures leads to ‎performance improvements.‎

According to the increasing in the usage of electricity, saving energy is so important. It can be occurred by both, optimizing consumption and the increasing of production efficiency. Small scale power plant (Distributed Generation (DG)) by reducing the distance of power production and consumption, declines a large part of this industry loss that is related to the distribution. The generators also have the ability to transform into the cogeneration system; As a result, by increasing efficiency, a great saving will happen in the production side of electricity industry. The issue of this article is the determination of priority of location of building DG. In this regard by using deviation from the optimum method and Human Development Index (HDI), according to data published by the Statistical Center of Iran and the Ministry of Energy, 9 provinces in the central area of Iran were selected and evaluated with 9 indicators. The results show that among the selected provinces the priority execution of power plant is first in Tehran, and then Isfahan.