مجله کیفیت و بهره وری صنعت برق ایران
سال هشتم شماره 1 (پیاپی 15، بهار و تابستان 1398)

In this paper a new algorithm for clearing of independent reactive power is presented. In the proposed algorithm both traditional objective function of reactive power and some new indices are optimised. The traditional objective function is minimizing the total cost of market and maximizing the profit of players. Some new indices such as Units Association Rate, Producer Unit Profit Rate, Profit Distributing Rate, Production Sharing  Rate and Risk of Work In Capacity Limits are introduced in this paper. These indices show the effectiveness of the proposed clearing strategy. The proposed algorithm is implemented on IEEE 24 bus test system.

Microgrid is a collection of electric loads, distributed generation units and battery storage operating as a controllable load which is able to supply the local loads. Air pollutions due to fossil fueled vehicles increase by factories development. Besides, shortage in fossil fuels increase the trends into electric vehicles. In this paper, optimal stochastic operation problem of microgrid in presence of electric vehicles and demand response loads is solved with the objectives of operation cost minimization and improving the security indices of the system in an acceptable level of the reliability. Besides, the optimal placement of PEV stations is performed. The proposed problem is formulated as mixed integer linear programming (MILP). Gams software is used as the simulation tool, and a 24-bus test system including a number of DGs, and PEV stations is studied to validate the efficiency of the proposed model

Due to the increasing importance of energy, optimizing power generation systems and reducing their internal energy consumption are essential issues. Analysis of power generation systems is necessary regarding energy and recognition of their functioning. The present study investigated the effect of media system on the performance and emissions of environmental pollutants in Thermoflow Software by modeling and simulating gas generators of Ansaldo Unit of Yazd Combined Cycle Power Plant. The results showed that Media System reduces the temperature of the inlet air to the compressor by 15 °C. Due to the constant volume of the inlet air, density increases and consequently discharge of the air inlet to the compressor increases by 20 kg/s. This results in an increase of 10 to 15 megawatts per generator and increases the efficiency of the gas cycle by 1%. One of the innovations in this research is the study of the effect of Media System on the emission of environmental pollutants. The results showed that this system decreases NOx and CO2 emissions by 20% per year, which can prevent social costs.

Due to the increasing use of microgrids, investigation of their stability is of special interest. One of the disadvantages of an inverter-based distribution unit is that at any given time, phase and frequency information at the point of common coupling (PCC) is required that can affect the stability. The synchronization techniques include synchronous reference frame phase-locked loop (SRF-PLL), virtual inertia, and constant K. To minimize cost and improve reliability, the minimum DC link capacitance should be determined. In this article, finding the minimum DC link capacitance is defined as an optimization problem where the particle swarm optimization (PSO) algorithm is used for the solving of the problem, where the stability is studied through eigenvalue analysis. In the stability analysis, harmonics and negative sequence of grid voltage, increasing the grid inductance, changes in the grid frequency, and changes in the solar irradiation intensity are considered. The effect of various synchronization methods on the stability of grid-connected inverters and DC link capacitance value is investigated.

Problems with the cost and pollution of fossil fuels have increased the incentive to operate on electric vehicles. However, the use of these vehicles is a challenge due to the additional loads which imposed on the power grid. Accordingly, a method has been proposed to improve the electrical parameters of the network including losses and voltage profiles by optimally managing the charge and discharge of plug in hybrid electric vehicles (PHEVs). The optimal management of the present paper involves the simultaneous management of active and reactive power of PHEVs. In order to implement the optimal management, in this paper, the probabilistic behavior of consumers and PHEVs are modeled on the factors affecting them. Regarding the multiplicity of factors considered and the non-convergence of the problem by conventional methods of optimization, a two-stage optimization method is proposed which provides the ability to achieve the desired goals by managing active and reactive power of PHEVs. The advantages of the proposed method can be to reduce the computational volume with respect to problem solving in each step of the time independently and thus reduce the optimization problem solving time. The proposed method is implemented by performing Monte Carlo repetitions on six power management scenarios implemented by GAMS and DIgSILENT software on real network of 20 kV distribution of Sirjan in Kerman province. The results of various scenarios show that the management of charge and discharge of PHEVs has smooth the voltage profile and reduced network losses. Therefore, using the proposed method, the additional loads imposed by the electric vehicle on the grid will not only increase the energy losses, but, with the proper management of the PHEVs, the network losses will be reduced compared to the absence of them.

In order to gain a deep understanding of planned maintenance, check the weaknesses of distribution network and detect unusual events, the network outage should be traced and monitored. On the other hand, the most important task of electric power distribution companies is to supply reliable and stable electricity with the minimum outage and standard voltage. This research intends to use time series and artificial neural network and propose some models to forecast the failure rate of equipment in the two regions controlled by Tehran Power Distribution Company. The data have been extracted weekly from the ENOX software from March 2012 to March 2016. To this end, after data pre-processing, the appropriate models have been provided using Minitab and MATLAB software. Moreover, the average air temperature, the average rainfall and the average wind speed were selected as inputs to the neural network. The mean square error (MSE) was used as a criterion to evaluate the error corresponding to the proposed models. The results revealed that time series models perform better than MLP neural network in forecasting equipment failure rates and thus they can be used for future periods.

The low fault current of high-impedance faults (HIFs) is one of the main challenges for the protection of distribution networks. The inability of conventional overcurrent relays in detecting these faults results in electric arc continuity that it causes the fire hazard and electric shock and poses a serious threat to human life and network equipment. This paper presents ​an HIF detection algorithm based on quantifying the nonlinear and asymmetry features of HIF waveforms. To this end, first, the substation current is sampled and then, the superimposed component of cross entropy signal calculated by comparing two subsequent half cycles of the current signal is determined as the HIF detection criteria. On the other hand, to overcome the challenge of the similar transient of device switching and HIF, the time duration of transient continuity is considered as the constraint of the proposed algorithm. The proposed scheme satisfactory works in the presence of nonlinear loads and does not need for training dataset, transformations, and calculation of harmonic and symmetrical components. The effectiveness of the proposed HIF detection algorithm is demonstrated using the time-domain simulation of IEEE 13 node test system.

Power grids are complex, interconnected and nonlinear systems, and this will be more severe when they are subjected to high wind resources penetration. Static synchronous compensators (STATCOM) are used to improve voltage regulation and to meet grid codes in power systems, including doubly fed induction generators (DFIG) based wind farms. Despite the nonlinear nature of STATCOM, the conventional control method in them is based on linear PI controllers with constant gains, which they cannot guarantee the optimal performance for STATCOM. In this paper, a comparison has been performed between two nonlinear control methods based on differential flatness theory for static synchronous compensators in the WSCC system includes DFIG based wind farms and simulation results demonstrate the effective performance of both methods in comparison with the conventional linear control method.

Distance relays are widely used to protect transmission lines. Sometimes, in these lines due to the occurrence of the swing of the power, the impedance calculated in the distance relay enters into its functional zones and leads to the cutting off of the lines. This issue can cause global power outages. Accordingly, in this paper, a Clark-based method for detecting the swing of power and distinguishing it from simultaneous faults is presented. The proposed method operates based on the calculation of the average current signal in the Clark transform output. This method is independent of network parameters and it is capable of detecting various types of swings, including stable and unstable power fluctuations and all types of simultaneous faults. To evaluate the proposed method, different modes of power swing and simultaneous errors are simulated in DIgSILENT software and then using the matrix data obtained from the current signal, the proposed algorithm is implemented in the Matlab software using the Clark transform. In addition to software evaluation, the proposed method has been tested using a tester and relay made by the Vebko Amirkabir knowledge-based company. The results of the software and practical tests show the success of the proposed method in identifying the types of power swings. The results also show that the proposed method has a high speed in detecting simultaneous faults with power swings.

In recent years, the utilization of clean renewable energies, especially solar energy, have increased dramatically due to their advantages over fossil fuels. Installation of photovoltaic systems is one of the most common methods of solar energy harvesting. The performance of these systems in converting solar energy to electricity is extensively a function of environmental conditions such as solar radiation, ambient temperature, wind, humidity, and other environmental parameters. Therefore, any changes in these parameters have a great influence on the design and performance evaluation of photovoltaic systems. Many regions with high solar potential for installation of photovoltaic panels are arid and deserted areas, in which the dust activities would significantly affect the performance of photovoltaic panels. This problem is much more serious in the climate of Iran, which is repeatedly faced with dust activities as well as aerosol dispersion. Therefore, the present study conducted a comprehensive review of the related literature in the field of the effect of dust on the performance and efficiency of solar panels. The results of the current study can serve as a thorough reference for researchers, designers, and engineers who deal with photovoltaic systems in regions struggling with dust events such as the Middle East, and in particular, Iran.