Abstract:Power distribution systems are profoundly inclined to disturbances like untimely switching of breakers & relays, sympathetic tripping, and uncertainties regarding fault location. Thus, system stability and reliability are greatly affected. In this way, situational awareness and system integrity are the crucial factors in developing power system security, as it empowers successful decision making & timely reaction by the operators to any disturbance and also maintaining continuity of power supply. This paper focuses on the enhancement of situational awareness by fault location through fault passage indicators (FPI) to improve nominal impedance-based methods in distribution networks. Also, the proposed method is validated by comparing it with Intelligent Electronic Device (IED) based fault location method. Further, simultaneous reconfiguration of the system is incorporated to maintain the continuity of supply. The analysis has been tested on IEEE 33 bus distribution system.

Abstract:In recent years, due to the economic and environmental issues, modern power systems often operate proximately to the technical restraints enlarging the probable level of instability risks. Hence, efficient methods for voltage instability prevention are of great importance to power system companies to avoid the risk of large blackouts. In this paper, an event-driven emergency demand response (EEDR) strategy based on whale optimization algorithm (WOA) is proposed to effectively improve system voltage stability. The main objective of the proposed EEDR approach is to maintain voltage stability margin (VSM) in an acceptable range during emergency situations by driving the operating condition of the power system away from the insecure points. The optimal locations and amounts of load reductions have been determined using WOA algorithm. To test the feasibility and the efficiency of the proposed method, simulation studies are carried out on the IEEE 14-bus and real Algerian 114-bus power systems.

Abstract:The increasing penetration of renewable energy sources (RESs) brings more power generation fluctuations into power systems, which puts forward higher requirement on the regulation capacities for maintaining the power balance between supply and demand. In addition to traditional generators for providing regulation capacities, the progressed information and communication technologies enable an alternative method by controlling flexible loads, especially thermostatically controlled loads (TCLs) for regulation services. This paper investigates the modeling and control strategies of aggregated TCLs as the virtual energy storage system (VESS) for demand response. First, TCLs are modeled as VESSs and compared with the traditional energy storage system (ESS) to analyze their characteristic differences. Then, the control strategies of VESS are investigated in microgrid and main grid aspects, respectively. It shows that VESS control strategies can play important roles in frequency regulation and voltage regulation for power systems’ stability. Finally, future research directions of VESS are prospected, including the schedulable potential evaluation, modeling of TCLs, hierarchical control strategies of VESS considering ESSs and RESs and reliability and fast response in frequency control for VESS.

Abstract:In this paper, we elaborate a new strategy based on cooperative game theory models to encourage and manage the interactions in a MicroGrid network. The proposed strategy optimizes the cooperation and the energy exchange in a distributed μGrid network. The strategy consists of a two stage algorithm: Coalition formation algorithm which was specifically created to approximate the optimal set of coalitions that return considerable savings. And the Matching game to manage the energy exchange inside each coalition. The performance of our strategy was verified through simulations. These latter show that the losses can be considerably decreased by the use of the proposed strategy: the rate of the loss reduction can reach up to 20% if the two stages are applied on the network. Moreover, the strategy proved to have a fast convergence which makes it operational for real implemented networks.

Abstract:Energy production from renewable sources offers an efficient alternative non-polluting and sustainable solution. Among renewable energies, solar energy represents the most important source, the most efficient and the least expensive compared to other renewable sources. Electric power generation systems from the sun’s energy typically characterized by their low efficiency. However, it is known that photovoltaic pumping systems are the most economical solution especially in rural areas. This work deals with the modeling and the vector control of a solar photovoltaic (PV) pumping system. The main objective of this study is to improve optimization techniques that maximize the overall efficiency of the pumping system. In order to optimize their energy efficiency whatever, the weather conditions, we inserted between the inverter and the photovoltaic generator (GPV) a maximum power point adapter known as Maximum Power Point Tracking (MPPT). Among the various MPPT techniques presented in the literature, we adopted the adaptive neuro-fuzzy controller (ANFIS). In addition, the performance of the sliding vector control associated with the neural network was developed and evaluated. Finally, simulation work under Matlab / Simulink was achieved to examine the performance of a photovoltaic conversion chain intended for pumping and to verify the effectiveness of the speed control under various instructions applied to the system. According to the study, we have done on the improvement of sliding mode control with neural network. Note that the sliding-neuron control provides better results compared to other techniques in terms of improved chattering phenomenon and less deviation from its reference.

Abstract:Accurate classification of power quality disturbance is the premise and basis for improving and governing power quality. A method for power quality disturbance classification based on time-frequency domain multi-feature and decision tree is presented. Wavelet transform and S-transform are used to extract the feature quantity of each power quality disturbance signal, and a decision tree with classification rules is then constructed for classification and recognition based on the extracted feature quantity. The classification rules and decision tree classifier are established by combining the energy spectrum feature quantity extracted by wavelet transform and other seven time-frequency domain feature quantities extracted by S-transform. Simulation results show that the proposed method can effectively identify six types of common single disturbance signals and two mixed disturbance signals, with fast classification speed and adequate noise resistance. Its classification accuracy is also higher than those of support vector machine (SVM) and k-nearest neighbor (KNN) algorithms. Compared with the method that only uses S-transform, the proposed feature extraction method has more abundant features and higher classification accuracy for power quality disturbance.

Abstract:In renewable penetrated power systems, frequency instability arises due to the volatile nature of renewable energy sources (RES) and load disturbances. The traditional load frequency control (LFC) strategy from conventional power sources (CPS) alone unable to control the frequency deviations caused by the aforementioned disturbances. Therefore, it is essential to modify the structure of LFC, to handle the disturbances caused by the RES and load. With regards to the above problem, this work proposes a novel coordinated LFC strategy with modified control signal to have Plug-in Hybrid Electric Vehicles (PHEVs) for frequency stability enhancement of the Japanese power system. Where, the coordinated control strategy is based on the PID controller, which is optimally tuned by the recently developed JAYA Algorithm (JA). Numerous simulations are performed with the proposed methodology and, the results have confirmed the effectiveness of a proposed approach over some recent and well-known techniques in literature. Furthermore, simulation results reveal that the proposed coordinated approach significantly minimizing the frequency deviations compared to the JAYA optimized LFC without PHEVs & with PHEVs but no coordination.

Abstract:Wind power forecasting is of great significance to the safety, reliability and stability of power grid. In this study, the GARCH type models are employed to explore the asymmetric features of wind power time series and improved forecasting precision. Benchmark Symmetric Curve (BSC) and Asymmetric Curve Index (ACI) are proposed as new asymmetric volatility analytical tool, and several generalized applications are presented. In the case study, the utility of the GARCH-type models in depicting time-varying volatility of wind power time series is demonstrated with the asymmetry effect, verified by the asymmetric parameter estimation. With benefit of the enhanced News Impact Curve (NIC) analysis, the responses in volatility to the magnitude and the sign of shocks are emphasized. The results are all confirmed to be consistent despite varied model specifications. The case study verifies that the models considering the asymmetric effect of volatility benefit the wind power forecasting performance.