COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING)
Year:2020 | Volume:10 | Issue:4 |Papers Count:8

In recent years, the use of photovoltaic generators for the production of electrical energy has become widespread. One of the important issues is the utilization of photovoltaic generators at the maximum output power point, considering the non-linear voltage-current characteristic of solar cells, is that various parameters affecting the voltage and current characteristics need to be controlled to reach the maximum output power point. This paper presents a nonlinear sliding mode controller for photovoltaic generator operation at the maximum output power point. The sliding surface is defined as the derivative of power with respect to the voltage, and the applied control signal to the system is extracted depending on the selected slide surface. The stability of proposed controller is proved using Lyapunov Method. Finally, numerical simulations are performed for different perturbations in MATLAB software and the performance of the proposed method is compared with the perturbation and observation methods. The results show the effectiveness of the proposed method as compared with the conventional perturbation and observation approaches.

In this paper, the modified bee optimization algorithm is proposed to appropriate switches placement regarding distributed generation to maximize the feeders’ reliability of a typical distribution network in Mazandaran. In this regard, actual network information including number of consumers, average consumption, feeders failures, feeders access time, repair time, etc. are used to determine network reliability indices. The optimal location of switches is determined by different scenarios due to the presence of distributed generation and the importance of feeders in the studied network. Moreover, the effect of cut-out fuses and ring points on feeders is considered in determining the appropriate switches placement, which is usually not considered in the reliability analysis of the distribution network and switches placement. Different indices are considered to determine the importance of feeders and a questionnaire has been prepared to utilize the personnel experiences of the distribution company to weigh these indices. Comparing and analyzing the results shows the ability of the proposed method in determining the appropriate switches locations in a real network.

In this paper, a single-stage soft-switching power-factor-correction (PFC) converter is proposed for driving two strings of LEDs. In the proposed driver, a transformer is added to an asymmetric series resonant half bridge converter to shape the input current. Also, zero-current-switching condition (ZCS) and zero-voltage-zero-current-switching (ZVZCS) condition are provided for switches at turn-on and turn-off, respectively, and near-unity power-factor is achieved for the converter. Moreover, the output currents are equal and independent of the output voltages which eliminates the need for current feedback or active current sharing method. Besides, the proposed driver provides minimum voltage for DC-bus capacitor and also switches voltage stress, while attaining low THD. Therefore, low voltage rating semiconductors with low on resistance and lower voltage capacitors with lower volume can be used. To validate the proposed driver features, operating principle of the proposed LED driver is presented, design considerations are discussed and experimental results of a laboratory prototype for supplying two 50 W/70 V strings of LED modules from 220 Vrms/50 Hz ac main is reported in this paper.

Nowadays, the approach of integrating low-cost inertial navigation system and satellite data is common due to accuracy and reliability matters. Self-reliance, high data set rates and rotational data presentation, in comparison to gradual reduced accuracy in inertial navigation systems, low data set rates, lack of rotational data presentation, and disruption or outage in the receipt of GNSS data has popularized the approach while Kalman filter integration methods face limitations such as model dependency, the need for prior knowledge, linearization, and most importantly, the inefficiency at GNSS signals outage. This paper aims to present a reliable intelligent online integration method that keeps functioning at signal interruption or outage. The results of GMDH neural network simulations and its comparison with the conventional Kalman filter method and MLP and RBF neural networks show that the former can be used in online navigation operations and for the inevitable conditions of GNSS data unavailability due to high speed and capability in estimating and correcting INS errors (because of having a simple structure and removing inactive neurons, through an effective learning method).

The P-V characteristic of a photovoltaic system is a nonlinear characteristic. The location and the value of the maximum power point in a Photovoltaic (PV) system depends on environmental conditions such as the intensity of solar radiation and the ambient temperature. To extract maximum power, a system including a DC-DC converter and a control method is required. Such a system is called maximum power tracking (MPPT), which is an essential part of a photovoltaic system. Model predictive control (MPC) is a type of control method which is used in MPPT. The model-based predictive control method detects the maximum power point using the equations of the dc-dc converter. In MPC, the system equations are ideally formulated. However, the values of the elements in the dc-dc converter change over time. There are two solutions to this problem. The first is to write real system equations. As a result, the formulations become very complicated and take up a large amount of microprocessor time and memory. Also, most of the time the exact values of the elements in the circuit are unknown. The second method is the use of additional sensors, which increases the cost of the system. In this article, a Fuzzy Model Predictive Control (FMPC) method is proposed for a Cuk converter to implement MPPT in photovoltaic systems. The proposed method obtains an improved performance and eliminates the need for the actual values of the elements. The method has been simulated in the MATLAB/Simulink workspace and its performance superiority has been demonstrated by comparing it against existing MPC and FL-MPPT approaches.

In recent years, the distributed generation resources (DGR) have become an essential and inseparable component in power networks and have achieved major success. Improving network power voltage, helping in peak load conditions and reducing load on power lines and transformers, utilizing renewable energies and reducing environmental pollution constitute some of the reasons for their expansion. In contrast to all these advantages in a given network, the increased presence of distributed generation resources has presented new challenges for power networks. One of the most important problems is the impact on system protection functionality. The traditional protection structure of the networks will be impaired and malfunctioned due to the presence of distributed generation resources. In this paper, a new intelligent linear control algorithm is used to restore the protective coordination. The proposed method modifies the protection characteristic curve of the protection system by adjusting the overcurrent relay current coefficient. This control procedure is in accordance with the widely applied overcurrent relay curves and complies with the relevant standards. For this purpose, by using a new drop control block and local sampling of over-current relay current, current setting of the relay is recovered to keep the correct operation of protection system. The proposed method is evaluated on two real micro-grids test system in Isfahan province by ETAP software. The findings indicate the ability of this method in coordinating the protection functionality of micro-grids with high penetration coefficient of DGR.