JOURNAL OF ENERGY MANAGEMENT
Year:2022 | Volume:11 | Issue:4|Papers Count:7

This paper designs an Optimized Adaptive Combined Hierarchical Sliding Mode Controller (OACHSMC) for a timevarying crane model in presence of uncertainties. Uncertainties have always been one of the most important challenges in designing control systems, which include unknown parameters or un-modeled dynamics in systems. Sliding mode controller (SMC) is able to compensate the system in the presence of uncertainties due to un-modeled dynamics and is used for robust stability and performance behavior in the presence of additive un-modeled dynamics of system and multiplicative friction forces. This under-actuated crane has two sub-systems: trolley and payload. Therefore, it can be controlled by a single input signal with combined hierarchical sliding mode controller (CHSMC) using a two-layersliding manifold accurately. Payload mass and cable length are time-variant variables through load transferring. Due to the Time-varying models and the inefficiency of most controllers, the use of an adaptive controller can help improve system performance. This controller is adapted by considering a time-varying coefficient of the second layer sliding manifold. For energy saving of the input signal, the parameter of the first layer sliding manifold of ACHSMC is optimized by two intelligent strategies: genetic algorithm (GA) and particle swarm optimization (PSO) method. The simulation results show robust stability and performance of the proposed optimized controller.

While current integrated gas-electric models usually ignore the potentials of demand response programs (DRPs) as an effective operation tool, this paper proposes a novel joint DRP to extend the feasibility of the integrated network operation. Moreover, the presented model considers the practical constraints of compressors and pipelines to construct a more accurate representation of the integrated network compared with the previous models available in the literature. To evaluate the proposed method, a set of scenarios representing a stressed integrated network is considered to simulate the main factors which limit the complete satisfaction of load requirements. The results of the conducted experiments in the considered case study suggest that the joint DRP can extend the boundaries of the feasible regions of the integrated system up to 8%, 28%, and 62% depending on the adopted scenario types. Especially, the simulation results indicate that the proposed model with joint DRP schemes can lead to more optimal solutions than the traditional ones which neglect DRPs.

Smart grids have been introduced to address power distribution system challenges. In conventional power distribution systems, when a power outage happens, the maintenance team tries to find the outage cause and mitigate it. After this, some information is documented in a dataset called the outage dataset. If the team can estimate the outage cause before searching for it, the restoration time will be reduced. In line with smart grid concepts, an association rule-based method is presented in this paper to find the outage cause. To do this, we have first combined outage, load, and weather datasets and extracted features. Then, for every cause, the records are labelled main class or others. The association rules are extracted and evaluated. Through these rules, one can determine whether the outage has happened because of a fault in a certain piece of equipment or not. Doing so alongside using smart devices may lead to reliability enhancement.

Wide Area Measurement Systems (WAMS) enable both real time monitoring and the control of smart grids by combining digital measurement devices, communication, and control systems. As WAMS consist of various infrastructures, they imply complex dependencies among their underlying systems and components of different types such as cyber, physical, and geographical dependencies. Although several works exist in the literature that studies cyber dependencies, other types of dependencies such as geographical dependencies have not yet been studied. In addition, there is a lack of dependency modeling methodologies that simultaneously capture different dependency types for WAMS. The main goal of this paper is a simultaneous modeling of the geographical and physical dependencies of WAMS infrastructures based on simple and well-defined rules. We define a probability density function to quantify these dependencies. Such a unified approach may support the design of WAMS infrastructures that are more resilient inherently to disruptions caused by different kinds of the unwanted events that may affect geographically dependent WAMS components. Through simulations, we demonstrate the applicability of the proposed methodology.

Microgrids have played an important role in distribution networks during recent years. DC microgrids are very popular among researchers because of their benefits. However, protection is one of the significant challenges in the way of these microgrids progress. As a result, in this paper, a fault detection and location scheme for DC microgrids is proposed. Due to advances in Artificial Intelligence (AI) and the suitable performance of smart protection methods in AC microgrids, Recurrent Neural Networks (RNNs) are used in the proposed method to locate faults in DC microgrids. In this method, fault detection and location are done by measuring feeders current and main bus voltage. Furthermore, the performance of the proposed method is assessed in grid-connected and the islanded operation modes of the microgrid. The result has confirmed the efficiency of the proposed scheme. In this paper, MATLAB and DIgSILENT are used to design RNNs and DC microgrid simulation respectively.

In this paper, a single-ended fault location method is presented based on a circuit breaker operation using the frequencies of traveling waves. The proposed method receives the required data from voltage traveling waves with the aid of Fast Fourier Transform (FFT) and Wavelet Transform. Then, the Artificial Neural Network (ANN) identifies the fault type and determines its location. For the evaluation of the proposed method, numerous simulations were done by varying parameters including fault resistance, fault inception angle, fault location, the presence of noise in waves, different sampling frequencies, and different structures of the power system in PSCAD/EMTDC software. Then, by using the matrix data obtained from voltage signals, the training process of the proposed algorithm is implemented in MATLAB software. The given results show the acceptable accuracy of the proposed technique in the classification of fault type and in the determination of fault location comparing with the previous studies. Also, the maximum error of the proposed method is 1. 29 percent. It stands for the robustness of the proposed scheme and is higher than those of the previous studies in the situations that may affect fault identification process.

Today, renewable energies are of more interest due to the depletion of the ozone layer, an increase in the cost of fossil fuels, and their friendliness with the environment. The sun is a great source of energy which is readily accessible. It has many applications in lighting, heating, and cooling. In this study the thermal performance of a solar ejector cooling cycle is evaluated using several different working fluids. The effect of operating conditions--namely the generator, condenser, and evaporator temperatures--on the system performance is also investigated. Based on weather conditions in different regions, the performance of ejector cooling cycle in several months of the year is also determined. The results show that the operating conditions have a determinant effect on cycle performance. It is observed that an increase in the generator exit temperature results in an increase in cycle coefficient of performance. It is also deduced that the rates of COP increase for the evaporator temperature ranges of 13-15°, C and 7-9°, C are about 8 and 6% respectively. It is also concluded that a larger collector area is required in warmer weather conditions.