A New Hybrid UPFC Controller for Power Flow Control and Voltage Regulation Based on RBF Neurosliding Mode Technique

This paper presents a new technique to design a Unified Power Flow Controller (UPFC) for power flow control and DC voltage regulation of an electric power transmission system which is based on a hybrid technique which combines a Radial Basis Function (RBF) neural network (online training) with the sliding mode technique to take advantage of their common features. The proposed controller does not need the knowledge of the perturbation bounds nor the full state of the nonlinear system. Hence, it is robust and produces an optimal response in the presence of system parameter uncertainty and disturbances. The performance of the proposed controller is evaluated through numerical simulations on a Kundur power system and compared with a classical PI controller. Simulation results confirm the effectiveness, robustness, and superiority of the proposed controller.

Comparative Analysis of Fuzzy Logic and PI Controller Based Electronic Load Controller for Self-Excited Induction Generator

Rural areas suffer from limited grid connectivity. Small hydroplants can provide electricity at a cheap cost with low environmental impact in these regions. Self-excited induction generators are widely used in hydroplants since they operate on a standalone basis because of the connection of capacitor bank that provides reactive power at no load. However, SEIGs suffer from poor voltage and frequency regulation. Thus, an electronic load controller (ELC) is connected across SEIG to regulate voltage and frequency. Generally, the control scheme for an ELC circuit is based on the conventional proportional integral control, which is easy to implement and performs well under linear load conditions. However, PI controllers handle nonlinearity poorly. This paper presents a fuzzy logic control (FLC) based control scheme for ELC in a constant power generation system (SEIG). The control scheme is designed and simulated in MATLAB under both linear and nonlinear load conditions. A comparison of both the controllers is conducted which highlights the superiority of the fuzzy logic control scheme.

Novel Basic Block of Multilevel Inverter Using Reduced Number of On-State Switches and Cascaded Circuit Topology

In this paper a basic block of novel topology of multilevel inverter is proposed. The proposed approach significantly requires reduced number of dc voltage sources and power switches to attain maximum number of output voltage levels. By connecting basic blocks in series a cascaded multilevel topology is developed. Each block itself is also a multilevel inverter. Analysis of proposed topology is carried out in symmetric as well as asymmetric operating modes. The topology is investigated through computer simulation using MATLAB/Simulink and validated experimentally on prototype in the laboratory.

Control Strategy for Power Loss Reduction considering Load Variation with Large Penetration of Distributed Generation

With the increase of penetration of distribution in distribution systems, the problems of power loss increase and short-circuit capacity beyond the rated capacity of the circuit breaker will become more serious. In this paper, a methodology (modified BPSO) is presented for network reconfiguration which is based on the hybrid approach of Tabu search and BPSO algorithms to prevent the local convergence and to decrease the calculation time using double fitness to consider the constraints. Moreover, an average load simulated method (ALS method) considering load variation is proposed such that the average load value is used instead of the actual load for calculation. Finally, from a case study, the results of simulation certify that the approaches will decrease drastically the losses and improve the voltage profiles obviously; at the same time, the short-circuit capacity is also decreased into smaller shut-off capacity of the circuit breaker. The power losses will not be increased too much even if the short-circuit capacity constraint is considered; voltage profiles are better with the constraint of short-circuit capacity considered. The ALS method is simple and the calculation time is fast.

Mitigating Congestion in a Power System and Role of FACTS Devices

Congestion management refers to avoiding or relieving congestion. In transmission lines, congestion management is one of the most important issues for the reliable operation of power system in the deregulated environment. Restructuring has brought considerable changes in all possible domains including electric supply industry. By virtue of restructuring, electricity has now become a commodity and has converted into a deregulated one. The traditional regulated power system has now become a competitive power market. In the present scenario, the real time transmission congestion is the operating condition in which the transfer capability to implement all the traded transactions simultaneously is not enough due to either some expected contingencies or market settlement. Thus, congestion is associated with one or more violations of the physical, operational, and policy constraints under which grids operate. Thus, congestion management is about managing the power transmission and distribution among valuable consumers priority-wise. Placement of FACTS (Flexible Alternating Current Transmission System) devices for generation rescheduling and load-shedding play a crucial role in congestion management. FACTS devices are used to enhance the maximum load ability of the transmission system. FACTS increases the flexibility of power system, makes it more controllable, and allows utilization of existing network closer to its thermal loading capacity without jeopardizing the stability. FACTS technology can boost the transfer capability in stability limited systems by 20–30%. As a result, more power can reach consumers with a shorter project implementation time and a lower investment cost. This review work unites the various publications on congestion management in past few decades.

Current Conveyor Based Window Comparator Circuits

This paper introduces a new window comparator circuit utilizing a new current conveyor and two diodes, operable at ±1.25 V and capable of accurately detecting the voltage windows. Another modified circuit with distinct binary levels suited for automatic control applications is also suggested. Exhaustive simulation results showing detection of windows, as small as 50 mV and as high as 1 V, are included. Comparisons are further drawn with the traditional operational amplifier based circuit and the new circuit is found to benefit from the use of current-mode active element, namely, Extra-X Current Controlled Current Conveyor. The proposed theory is well supported through simulation results.

Fuzzy Logic Controller Based Distributed Generation Integration Strategy for Stochastic Performance Improvement

In the restructured environment, distributed generation (DG) is considered as a very promising option due to a high initial capital cost of conventional plants, environmental concerns, and power shortage. Apart from the above, distributed generation (DG) has also abilities to improve performance of feeder. Most of the distribution feeders have radial structure, which compel to observe the impact of distributed generations on feeder performance, having different characteristics and composition of time varying static ZIP load models. Two fuzzy-based expert system is proposed for selecting and ranking the most appropriated periods to an integration of distributed generations with a feeder. Madami type fuzzy logic controller was developed for sizing of distributed generation, whereas Sugeno type fuzzy logic controller was developed for the DG location. Input parameters for Madami fuzzy logic controller are substation reserve capacity, feeder power loss to load ratio, voltage unbalance, and apparent power imbalances. DG output, survivability index, and node distance from substation are chosen as input to Sugeno type fuzzy logic controller. The stochastic performance of proposed fuzzy expert systems was evaluated on a modified IEEE 37 node test feeder with 15 minutes characteristics time interval varying static ZIP load models.

Intelligent Fault Diagnosis in a Power Distribution Network

This paper presents a novel method of fault diagnosis by the use of fuzzy logic and neural network-based techniques for electric power fault detection, classification, and location in a power distribution network. A real network was used as a case study. The ten different types of line faults including single line-to-ground, line-to-line, double line-to-ground, and three-phase faults were investigated. The designed system has 89% accuracy for fault type identification. It also has 93% accuracy for fault location. The results indicate that the proposed technique is effective in detecting, classifying, and locating low impedance faults.

CPW Fed Miniaturized UWB Tri-Notch Antenna with Bandwidth Enhancement

A Coplanar Waveguide (CPW) fed miniaturized ultra-wideband (UWB) tri-notch antenna with bandwidth enhancement is proposed. The antenna is prototyped for ultra-wideband (UWB) communication applications with bandwidth enhancement. By implementing asymmetric structure, the miniaturization in the antenna has been achieved with a very compact size of 22 × 13 × 1.5 mm3 making it suitable for USB dongle applications. The antenna is first simulated with partial rectangular ground plane which shows that operating bandwidth ranges from 3.1 to 10.6 GHz. Then the antenna is measured and simulated by cutting the ground plane with specific geometry which shows that operating bandwidth now ranges from 3.1 to 20 GHz which provides a wide usable fractional bandwidth of more than 150%. The antenna is then modified for tri-notch applications in order to reject worldwide interoperability for microwave access (WiMAX) band (3.3–3.6 GHz) and wireless local area network (WLAN) frequency bands (lower WLAN (5.15–5.325) and upper WLAN (5.725–5.825) GHz). Triple Band rejection capability has been achieved by introducing three complementary split ring resonators (CSRR) in the radiating patch.

Assessment of Global Voltage Stability Margin through Radial Basis Function Neural Network

Dynamic operating conditions along with contingencies often present formidable challenges to the power engineers. Decisions pertaining to the control strategies taken by the system operators at energy management centre are based on the information about the system’s behavior. The application of ANN as a tool for voltage stability assessment is empirical because of its ability to do parallel data processing with high accuracy, fast response, and capability to model dynamic, nonlinear, and noisy data. This paper presents an effective methodology based on Radial Basis Function Neural Network (RBFN) to predict Global Voltage Stability Margin (GVSM), for any unseen loading condition of the system. GVSM is used to assess the overall voltage stability status of the power system. A comparative analysis of different topologies of ANN, namely, Feedforward Backprop (FFBP), Cascade Forward Backprop (CFB), Generalized Regression (GR), Layer Recurrent (LR), Nonlinear Autoregressive Exogenous (NARX), ELMAN Backprop, and Feedforward Distributed Time Delay Network (FFDTDN), is carried out on the basis of capability of the prediction of GVSM. The efficacy of RBFN is better than other networks, which is validated by taking the predictions of GVSM at different levels of Additive White Gaussian Noise (AWGN) in input features. The results obtained from ANNs are validated through the offline Newton Raphson (N-R) method. The proposed methodology is tested over IEEE 14-bus, IEEE 30-bus, and IEEE 118-bus test systems.