مجله کیفیت و بهره وری صنعت برق ایران
سال دوازدهم شماره 2 (پیاپی 31، تابستان 1402)

To maintain supply-demand balance, it is crucial to design a method to provide prior knowledge on load consumption in look-ahead time with high level of accuracy and reliability. The load prediction problem is becoming more and more challenging due to emerging new concepts in the electrical grids and reconstruction of the power networks. This paper develops a residual neural network to predict the electrical loads with high level of accuracy. In the designed network with combining two powerful deep residual network, a new residual deep network is proposed to improve the learning ability as well as prevent problems like overfitting and gradient reduction/explosion. Furthermore, to fully understand the spatial-temporal features, convolutional neural network (CNN) and gated recurrent unit (GRU) are combined and integrated into the designed multi-level deep network. The seasonal analysis and investigating several cases using actual electrical load consumption in Shiraz, Iran verifies the effectiveness of the proposed method and higher accuracy of the proposed deep network in comparison with other methods demonstrate the superiority of the proposed method.

In this paper, the optimal operation of micro-grid in the presence of thermal block, distributed generation, storages and demand response to achieve the optimal scheduling of active, reactive and thermal power of these elements in the day-ahead energy and reactive power markets is presented. The thermal block includes combined heat and power system, boiler, and thermal demand response. This scheme minimizes the difference between total operation cost of micro-grid and sources, and total revenue obtained from the mentioned markets. It subjects to AC power flow equations, network operation limits, and operation model of these elements. Moreover, the scheme includes uncertainties of energy price, load and renewable power, where unscented transformation method models these uncertainty parameters. Finally, by implement of the proposed scheme on the 119-bus radial micro-grid, the obtained simulation results confirm the capability of the scheme in the improving of economic and operation situation of micro-grid.

Adverse weather conditions and natural disasters always inflict extensive losses and outages in distribution networks while the number and severity of these incidents have often been on the rise in recent years. Therefore, evaluating the resilience of the network and its reversibility in the face of adverse weather conditions and reducing the permeability of the electricity distribution network exposed to natural disasters should be among the planning priorities for the design and optimal operation of these networks. T​this study aims to construct new tie lines between the healthy part and the damaged parts of a network in the event of a possible accident to quickly restore service to the parts that have lost electricity. The paper presents an innovative iteration-based method that uses graph theory to optimize the total objective function, including the cost of constructing tie lines, the cost of reliability, and the cost of resilience. The proposed method was applied to a part of the RBTS test system, and the effect of constructing new tie lines was investigated on the resilience and reliability indicators of the network according to the cost of their construction

Solar energy, as an unlimited, clean and cost-effective source of energy, can be converted into electrical energy using photovoltaic arrays. In this paper, design, control and stability analysis of inverter-based power conditioner, which is connected to the low voltage grid via LCL filter, are presented to manage power injection of the photovoltaic system. Since the PV arrays generated voltage is lower than the grid voltage, DC-DC converter is utilized to increase the PV output voltage. The LCL filter is employed to improve injected current quality and to reduce interface inverter output voltage distortions. This filter can cause to occur resonance and system instability. Current double feedback control method, which is used inverter side and grid side currents as feedback current, is proposed to damp the resonance and to improve harmonic eliminations. The systematic design procedure of the proposed control strategy parameters is presented considering resonance damping, stability margin and grid-connected features to ensure the system stability in a non-ideal and weak grid. The proposed power conditioner system is simulated in MATLAB/Simulink to verify model performance and proposed control method. The simulation results show good performance of the proposed system in managing PV power flow into the grid, high quality of the injected current, and system stability against the grid impedance change disturbances.

The problem of determining the optimal capacity and location of distributed generation resources is one of the important topics in the design and operation of power systems. To address this issue, this paper proposes a multi-objective developed model for optimal allocation of solar resources in radial distribution systems based on objective functions such as improving voltage profile, reducing losses, and maximizing penetration level. The optimal values, in other words, the capacity of solar resources to meet the optimal voltage profile and minimize losses under high penetration levels of these resources, have been obtained. Since these objectives are conflicting, a multi-objective developed algorithm called Gray Wolf Optimizer has been proposed to solve them simultaneously. Compared to other multi-objective problem-solving methods, the proposed Gray Wolf Optimizer demonstrates a high capability in solving multi-objective problems and finding Pareto fronts, while avoiding local optima. Additionally, in order to enhance the capabilities of the Gray Wolf Optimizer, a social hierarchy-based modified method has been employed to reduce solution time and improve the allocation matrix. Finally, the proposed method and the intended model have been evaluated on a standard system under various operating conditions. The obtained results show that the proposed method has been able to maintain an acceptable voltage profile and significantly reduce losses compared to other multi-objective algorithms. For low to medium penetration levels, losses tend to decrease until reaching a minimum value, and for penetration levels above 100%, losses increase. Furthermore, at a penetration level of 300%, the efficiency of the system has improved by about 12% in terms of voltage profile using the optimal allocation, indicating the excellent efficiency of the proposed method even at high penetration levels. Additionally, it has been demonstrated that in comparison to other multi-objective optimization methods, the proposed method has performed well in terms of the inverted generational distance parameter.

Accurate fault location in a transmission line system is very important for electric utilities in order to quickly diagnose the location of the fault. Using a suitable technique for accurate fault location effectively reduces the time of fault recovery and system operation cost during maintenance. As a result, the reliability and quality of electricity delivery is improved and the economic losses caused by line outages are reduced. In this paper, an accurate two-terminal traveling wave-based fault location method for transmission lines is proposed. In this method, based on the arrival time of the first and second traveling waves caused by the short circuit fault, which are measured separately at both terminals of the transmission line, fault location and fault inception time are determined. The proposed algorithm does not need to synchronize data, transmission line  parameters, and traveling wave speed, which are sources of error in traveling wave-based fault location methods.  In order to better analyse fault-induced transient signals and detects the arrival time of travelling waves, mathematicl morphology filter (MMF) is used. Several faults on a typical 400 kV, 200 km transmission line were simulated using EMTP  and MATLAB programs. The simulation results verified the proposed algorithm is able to accurately locate faults on transmission line. Also, the proposed method is independent of fault conditions shch as fault impedance, fault type, fault inception time and fault location.

Integrating large-scale wind farms with the main grid poses several challenges to operating the power utilities. One of the most important is to extract the maximum possible power in different operating conditions from the installed capacity. One of the common solutions in wind power plants equipped with doubly-feed induction generators is to control the output power of the rotor by using a proportional-integral controller.In this paper, a modified chaos-based imperialist competition algorithm is used to manage the control coefficients of twenty wind turbines (2 MW) located in a microgrid. The results show that the use of chaos theory improves the quality of convergence tracking of maximum power points during rapid changes in wind speed. In the classical proportional-integral controller, when the wind speed changes rapidly, the generated torque contains a large ripple and this controller is unable to overcome the nonlinear and ripple torque properties. This weakness increases the stress on the system and can damage the structural equipment of the generator. At the same time, the results obtained using the chaotic-based imperialist competition algorithm show that not only the ripple content is significantly reduced, but also more than 40% of its subsurface value is reduced.