Volume 13, Issue 3

In this research paper, predictive modelling of NOx emission of a 210 MW capacity pulverized coal-fired boiler and combustion parameter optimization to reduce NOx emission in flue gas is proposed. The effects of oxygen concentration in flue gas, coal properties, coal flow, boiler load, air distribution scheme, flue gas outlet temperature and nozzle tilt were studied. The data collected from parametric field experiments were used to build a feed-forward back-propagation artificial neural net (ANN). The coal combustion parameters were used as inputs and NOx emission as outputs of the model. The ANN model was developed for full load condition and its predicted values were verified with the actual values. The algebraic equation containing weights and biases of the trained net was used as fitness function in sequential quadratic programming (SQP) to find the optimum level of input operating conditions for low NOx emission. The results proved that the proposed approach could be used for generating feasible operating conditions.

The Levenberg-Marquardt (L-M) method and the path following interior point (PFIP) method are two most popular methods to solve the optimal power flow (OPF) problem. The performance comparison between the L-M method and the PFIP method applied to solve the OPF problem has not been made so far. The main objective of this paper provides a more comprehensive reference for researcher by analysing, comparing and evaluating the performances of the two methods. The L-M method is employed to solve the OPF problem formulated by the nonlinear complementarity problem (NCP). After the Karush-Kuhn-Tucker (KKT) conditions of OPF model are represented in term of a set of semismooth equations by introducing the NCP function, the semismooth equations are solved by the L-M method. The PFIP method, combined logarithmic barrier function with Newton-Raphson method, has been then applied to solve the OPF problem. Finally, the two methods are tested on the 5-bus, the IEEE 30-bus and the IEEE 118-bus system, respectively. Simulation results show that the two methods get the same optimal value of the objective function under the convergent criterion, while the L-M method has higher computation precision and faster convergence speed than the PFIP method.

A new methodology for a possible fast evaluation of a power flow is presented. The proposed method involves a transient stability constrained line-flow-based (LFB) fast contingency screening. This ‘direct’ method incorporates transient energy function (TEF) to carry out the evaluation of a power system. The proposed LFB method was tested on the WECC 9-bus, three machine, the IEEE 14-bus, five machine, and the IEEE 39-bus, ten machine systems. Results of the proposed LFB were compared with those obtained from the conventional Newton-Raphson (N-R) method. It was observed that the stable re-dispatch could be obtained by the LFB method within half the computation time as compared to the time required for the N-R method. Since the LFB method was based upon identification of the stable equilibrium points of the TEF, the proposed method appears to be inherently capable of delivering a stable power dispatch. In addition, a new index called critical energy performance index (CEPI) has been introduced to indicate the level of stability and to assist in the decisions related to the re-dispatch.

In this paper an intelligent-based approach is introduced to evaluate harmonic overvoltages during three-phase transformer energization. In a power system that appears in an early stage of a black ‎start of a power system, an overvoltage could be caused by core ‎saturation on the energization of a three-phase transformer with residual flux. ‎Such an overvoltage might damage some equipment and delay ‎power system restoration. A new approach based on worst case determination is proposed to reduce time-domain simulations. Also, an artificial neural network (ANN) has been used to estimate the temporary overvoltages (TOVs) due to three-phase transformer ‎energization. ‎ Three learning algorithms, delta-bar-delta (DBD), extended delta-bar-delta (EDBD), and directed random search (DRS), were used to train the ANNs. ANN Training is performed based on equivalent circuit parameters of the network; thus trained ANN is applicable to every studied system. The ‎developed ANN is trained with the worst case of the switching condition and remanent flux, and ‎tested for typical cases. The simulated results for a partial of 39-bus New England test system, ‎show that the proposed technique can estimate the peak values and ‎durations of switching overvoltages with good accuracy and EDBD algorithm presents best performance.

A V2G facility has the potential to reduce losses and loading in the power distribution network, thereby effecting savings and an increase in the generator, line and substation capacity for additional loading. In this paper, economic models are developed to compute the amount of economic incentives accruable from the penetration of V2G into the distribution network subject to two potential benefits of V2G: released generation capacity and reduced energy losses. The developed models were tested using IEEE test systems. Results from the test systems reveal that operational choice affects economic incentives. Hence, further analytical expression was developed to model operational formulation that will lead to economic incentives. This principal model was formulated to indicate the manner in which economic incentives can be impacted by decision variables, namely: loading pattern, V2G location and capacity injection. More than 95% released generation capacity was obtained. In addition, $37,775/year of economic incentive due to reduced power losses in the IEEE 123 Node test Feeder was observed. The results from the principal model showed that V2G promises significant economic incentives. It may be concluded, based on the results obtained, that proper system studies are necessary at the planning stage before installing a V2G facility.

This paper presents the operation under fault and protection of multi DC smart grids connected to the DC transmission line. The smart grid consists of wind turbine and photovoltaic generators, controllable loads, generator side and grid side inverters. The voltage stability of the DC bus voltage is attained by applying the power consumption control of controllable loads, the power control of grid connected converter and power control of renewable plants. On the other hand, the fault protection for short-circuit and low resistance ground fault at the DC transmission line was sighted. Simulation results based on PSIM are provided to demonstrate the effectiveness of the system.

The introduction of wide area measurements has brought a need for real time assessment methods of power systems, which are accurate and fast. The time varying coefficients in synchronous machine equations make it difficult to find solutions to obtain machine voltages, currents and flux linkages when expressed in phase quantities under transient conditions. The paper presents an approach to design power system transient stability assessment using direct methods for a multi-machine network based on multiple synchronized phasors, measured from Phasor Measurement Units (PMUs) and generator parameters. The generator rotor angle was derived from phasor measurements of voltage and current, and generator parameters using direct algorithm . The method assumes that a temporary fault is applied to the system therefore the pre-fault and post-fault conditions are similar. The multi-machine system was reduced to groups denoted Single Machine to Equivalent Bus (SMEB) models and another groups denoted Load Equivalent Bus (LEB) using Parallel Algorithms (PAs) [1]. The use of these PAs eliminates the SPMUs at each bus in the system, and it is required number of SPMUs only equals the number of generator buses. So that, the Equal Area Criterion in both rotor angle domain and time domain can be applicable for the SMEBs groups to assess the system stability in real-time through the Synchro-Phasors Measurements Units (SPMUs). A temporary three phase fault was simulated at test system comprises 2-machine, 8-bus network for validating the novel algorithm.

Wide use of non-linear loads results in harmonic propagation throughout the entire power system. The harmonics generated in the power system by the harmonic injection buses need to be properly measured and quantified using minimal information about the power system network. Independent Component Analysis (ICA) provides several algorithms for harmonic state estimation, some of which are more accurate at specific harmonic frequencies. In this paper, the best ICA algorithm for steady state performance (i.e. the algorithm with the least error) is chosen and the resulting mixing matrix is processed by a Kalman Filter which functions as an optimal estimator. The harmonic state estimation is implemented on a simulated four bus system and a laboratory four bus model is also wired and the results of the work are presented.

This paper presents a multi-class support vector machine (SVMs) approach for locating and diagnosing faults in electric power distribution feeders. Different from the traditional Fault Section Estimation methods, the proposed approach uses measurements available at the substation and remote terminal units (RTUs). To show the effectiveness of the proposed methodology, a practical 52 nodes, 3 feeder distribution systems (DS) with loads is considered. Practical situations in distribution systems, such as protective devices (circuit breakers/isolators) placed at different locations and all types of faults with a wide range of varying source short circuit (SSC) levels and fault impedances are considered for studies. The proposed fault location scheme is capable of accurately identify the fault type, location of faulted feeder section and the fault impedance. The results demonstrate the feasibility of applying the proposed method in practical distribution automation (DA) system for fault diagnosis.