Journal of Operation and Automation in Power Engineering
Volume:12 Issue: 3, Autumn 2024

In this article, a novel topology of DC-DC converter based on voltage multiplier cell and coupled inductor with higher efficiency and low blocking voltage across semiconductor is proposed for renewable energy application. The recommended topology obtains a high voltage gain using voltage multiplier cell and one coupled inductor. Only one power switch is utilized in this structure, which reduces the converter's cost. The other benefits of this converter are low number of components, high efficiency due to the zero-voltage switching and the zero-current switching of diodes, and low blocking voltage of the power switch and diodes. Besides, the voltage multiplier cell acts as a passive clamp circuit and reduces the voltage stress across the power switch. Thus, a low rated power switch can be used in the presented topology. Due to the zero-current switching in Off-state, the reverse recovery problem of diodes is reduced. To illustrate the performance and superiority of the presented topology, operation modes, steady-state and efficiency analysis, and the comparison study with other similar converters are presented. Finally, a 160~W experimental prototype with 50~kHz switching frequency and 17 V input voltage are built to confirm the theoretical investigation and effectiveness of the proposed converter.

Microgrids are known as the main components of energy networks because they can accommodate a large share of renewable energy sources. Peer-to-peer energy trading is one of the most effective ways to implement decentralized patterns in the electricity market. In peer-to-peer trades, each actor negotiates directly with a set of partners without any intermediaries. Peer-to-peer energy exchange methods allow direct energy exchange between producers and consumers. This study tested the peer-to-peer trading method on networks consisting of 4 microgrids. Existing microgrids have different generating sources, such as solar energy, wind turbines, and microturbines, each of which is modeled separately. Moreover, in order to reduce the uncertainty in the production of renewable sources, a battery storage system has been used in this network. Also, to encourage microgrids to use renewable resources, cut-off costs have been considered by these resources. This research uses the constrained optimization method and GAMS software with a Baron solver to optimize the problem. In the end, the uncertainty of producing renewable resources for different modes is examined using the information gap decision theory method. The available results show the power distribution between microgrids and other network components based on the objective function and existing constraints.

This paper proposes a complete diagnostic analysis of faults in a typical modern power system's transmission line using the support vector machine (SVM) with time-series parameters and frequency series parameters as features. The training and testing data of the proposed method are collected by simulating all types of faults with all possible variations on a transmission line (TL) in the IEEE-9 bus system using the PSCAD/EMTDC software. While simulating one type of fault, fault resistances and fault inception angles are also varied to account for the various behaviours of the fault. The three-phase instantaneous currents and voltages on both sides of TL are recorded at 32 samples per cycle. A thirty-two sample moving window is used to compute time-series and frequency-series parameters applied as features to the SVM. Ten-fold cross-validation is used to evaluate the performance of the proposed algorithm with evaluation metrics such as accuracy, precision, recall and F1 score. Features generation, training and testing of the proposed method, and performance comparison are done using PYTHON software. The proposed method has achieved an average accuracy of 99.996%, even in the most contaminated environment of 30 dB noise. Compared with the performance of the other popular machine learning algorithms, the proposed method has achieved more accuracy. The performance of the proposed method is also tested with different noise levels, which account for the measurement errors of 30 dB, 35 dB and 40 dB.

Thermoelectric generators (TEGs) can transform wasted heat from industrial processes into electrical power. The power provided by TEGs systems depend on the temperature gradient, where an ideal situation for the TEGs operation is when all the modules of an array are exposed to the same temperature difference. Unfortunately, that condition is not always possible since the TEG arrays are exposed to non-uniform thermal conditions (known as mismatching). This paper proposes a novel equivalent model to represent the electrical behavior of a TEG, including a high-order approximation for the temperature dependence properties of the internal resistance and output voltage. Several configurations proposed to mitigate the mismatching phenomenon on TEGs arrays were tested, which are based on boost converters, PI controllers and the perturb and observe algorithm for maximum power point tracking: 1) TEGs serial connection with a single power converter, 2) a parallel connection where each TEG has its own converter, and 3) a serial connection where each TEG has its own converter. Those tests were performed in three temperature differences (50°C,  100°C and 180°C) to study the impact of the mismatching thermal condition over the total output power. The maximum power delivered by the traditional case 1 was 10.7 W; while the output power provided by case 2 was 12.07 W (12.8 % higher) and 11.1 W (3.7 %) for case 3.

Transformers are one of the most important parts of the electric transmission and distribution networks, and their performance directly affects the reliability and stability of the grid. Maintenance and replacing the faulted transformers could be time-consuming and costly and accordingly, a solution should be proposed to prevent it. This led to studies in the field of transformer lifetime management. As a result, estimating the remaining lifetime of the transformer is a crucial part for the mentioned solution. Therefore, this paper aims to tackle this issue through employing a new algorithm to estimate the lifetime of a transformer by combining selection methods and Artificial Intelligence (AI)-based techniques. The main goal of this method is to reduce the estimation error and estimation time simultaneously. The proposed approach assesses transformers based on environmental conditions, power quality, oil quality, and dissolved gas analysis (DGA). Consideration of additional factors overcomes the disadvantage of traditional methods and gives a meticulous result. In this respect, the collected data from the power transformer of Iran and Iraq as well as regions with different conditions are employed in the studied algorithm. Several combinations of algorithms are investigated to choose the best one. Principal Component Analysis (PCA) is employed in the next step for weighing the various parameters to improve the accuracy and decrease execution time. Results show that the Bayesian neural network provides the best performance in the predicting remaining lifetime of the transformer with an accuracy about 98.4%.

With the exponential penetration of renewable energy sources (RES), the need for compatible scheduling of these has increased from economic and environmental points of view. Due to the high-efficiency and fast-response features of combined heat and power (CHP) generation units, these units can immunize the system against RES fluctuations. To address the operational challenges associated with RES, this paper aims to schedule the arbitrage of cryogenic energy storage (CES) not only to maximize its owner but also to minimize RES variability. On the other hand, plug-in electric vehicles (PEV) are applied in the proposed model as responsible loads to smooth the system's load profile by changing the consumers' consumption patterns. The proposed problem is modeled as second-order cone programming and solved by the dominated group search optimization algorithm. To verify the applicability and effectiveness of the proposed approach, four different case studies have been executed.

The purpose of this study is to present a practical approach in which the effect of performance degradation and instability factors such as exogenous disturbances, parametric uncertainties, time-varying delay, and unstable modes can reduce to the minimum possible amount in linear switched positive systems. To reduce the effect of the mentioned destructive factors and to strengthen the robust design of switched positive systems, in this paper, instead of using the co-positive Lyapunov function along with the L1-gain, the quadratic Lyapunov-Krasovskii function utilized along with the L2-gain, which leads to the design of H_∞ performance. The latter method, especially when there is a requirement to estimate the parameters with the support of the output feedback approach by minimizing the interface parameters, provides the feasibility of designing a more convenient and efficient observer-based controller. The necessary and sufficient conditions for solving the problem concerning the positivity of the system, disturbance attenuation, and parametric uncertainties are expressed by two theories and implemented by the linear matrix inequality technique. The results of this technique's solution include the gains of the controller and observer. Considering that stable and unstable modes are in this system, it is necessary to guarantee the exponential stability of the whole system by the controllers and designing the average dwell-time switching regime. Finally, illustrative examples, including numerical, practical, and comparative, are presented to show the efficiency and performance of different aspects of the proposed approach. The smallness of the mean square error values in the example compared with the output feedback method in linear programming confirms the capabilities of the presented approach. For instance, the mean square error of the system output for the method of this paper is 0.008 and for the compared approach is 0.081.

In this paper, Renewable energy sources (RES) are incorporated into the electricity grid. A real-time Andhra Pradesh 14 bus system is considered in which, windy and sunny locations are identified for this study. A new algorithm called Persistence - Extreme Learning Machine (P-ELM) is suggested. The suggested methodology is used to predict wind speed and solar insolation in the selected regions across the short-term and long-term time period horizons. The load flow problem is handled in 12 distinct by penetrating the wind and solar power into the system. The research findings are examined in terms of voltage variation and active power loss. The results obtained observed as, with wind and solar integration, the voltage variation is higher in both the short and long-term time frames, but the active power losses are lower than in the other cases.