Independence of error terms in a linear regression model, often not established. So a linear regression model with correlated error terms appears in many applications. According to the earlier ...studies, this kind of error terms, basically can affect the robustness of the linear regression model analysis. It is also shown that the robustness of the parameters estimators of a linear regression model can stay using the M-estimator. But considering that, it acquires this feature as the result of establishment of its efficiency. Whereas, it has been shown that the minimum Matusita distance estimators, has both features robustness and efficiency at the same time. On the other hand, because the Cochrane and Orcutt adjusted least squares estimators are not affected by the dependence of the error terms, so they are efficient estimators. Here we are using of a non-parametric kernel density estimation method, to give a new method of obtaining the minimum Matusita distance estimators for the linear regression model with correlated error terms in the presence of outliers. Also, simulation and real data study both are done for the introduced estimation method. In each case, the proposed method represents lower biases and mean squared errors than the other two methods.
The growth in the energy demand of the microgrid due to the inclusion of electric vehicles (EV) and other non-EV loads introduces several challenges for the operators in scheduling energy for the ...microgrid. The inclusion of demand response (DR) program in the operational planning of microgrid can decrease the burden on the operator, but it requires aggregators for the efficient coordination between the operator and several potential DR participants of the microgrid. In this work, an optimization model is proposed to include a novel incentive-based DR program in the energy management problem of the reconfigured grid-connected microgrid. Two different aggregators for EV and non-EV loads are included in the work as an interface between the operator and DR participants. The objective of the proposed DR program is to maximize the incentives offered to the DR participants while maintaining uniformity in terms of rewards and distress delivered to the DR participants. The proposed work is analyzed on a static model of a 33-bus grid-connected microgrid consisting of EV charging stations, renewable energy sources, and diesel generators at different locations. The microgrid is reconfigured at each operating interval to minimize the power lost in the network. The result confirms that optimality is achieved at the source, distribution, and load side of the microgrid. For a day-ahead operation, it has been found that the energy dependency of a microgrid on the utility grid and conventional energy source is reduced by 9.62% and 29.06%, respectively.
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•Optimal coordination of aggregators in the reconfigured grid-connected microgrid.•Proposal of novel method to reward the participants of demand response program.•Energy management issue due to pre-fixed power exchange between grid and microgrid.•Application of improved JAYA algorithm in the energy management issue of microgrid.•Analysis of uncertainties in DR participants using Hong’s point estimation method.
•Integration of demand response and reconfiguration for energy schedule of microgrid.•Participation of energy consumers in energy management problem of microgrid.•Consideration of uncertainties in ...renewable sources using point estimation method.•Fixed power exchange between grid and microgrid using feeder flow control mode.
Demand response (DR) programs and reconfiguration of distribution networks are generally adopted in the energy management (EM) problem of microgrid to enhance the technical and economical features of microgrid. Assuming a fixed configuration of distribution network, DR programs usually optimize the generation cost by encouraging the consumers to reduce their energy demands. Whereas reconfiguration of network is done for a pre-defined generation schedule and energy demand. However, separate incorporation of these two operational techniques in the EM problem may lead to a non-optimal solution. In this paper, a joint framework is proposed to integrate a novel incentive-based DR program and reconfiguration method in the EM problem of microgrid on a day-ahead time frame. The objective of the work is to minimize the fuel cost of conventional distributed generation (DG) and the cost of power purchased from the grid, while maximizing the profit for microgrid operator (MGO). The efficacy of the proposed model is tested on a static model of grid-connected 33-bus microgrid which consists of renewable energy (RE) sources and a conventional DG. To account the uncertainties in RE sources, Hong’s (2m+1) point estimation method (PEM) is considered in this work. The result confirms that the incorporation of DR program and reconfiguration method in the EM problem leads to an optimum energy schedule for the microgrid with a minimum lossy network. For the single-day operation of microgrid, it has been found that the power transfer from the grid and power lost in the network is reduced by 10.83% and 34.03% respectively.
When a small number of snapshots are used, the performance of the direction-of-arrival (DOA) estimation method based on the least squares (LS) degrades severely because of inadequate estimation of ...the covariance matrix. Although the subspace-based DOA estimation methods were proposed to improve the performance of DOA estimation method based on the LS; however these methods are computationally complex, especially for a large number of array elements. In this Letter, the DOA estimation method based on the LS is improved by reconstructing the covariance matrix with diagonal loading, where the diagonal loading factor is computed automatically by estimating the signal power. The reciprocal of the array pattern is taken to calculate the spatial spectrum, where the peak values correspond to the estimated DOAs of signals. The proposed method can achieve better performance with few snapshots and low computational complexity. The effectiveness of the proposed method is verified by the numerical simulations.
Voltage sag frequency estimation is necessary for understanding the voltage sag severity in power system and offering full information for the interested parties to mitigate voltage sag. The high ...penetration of wind power in the power system and the uncertainty of the fault distribution raise new challenges to accurate voltage sag frequency estimation. This study presents a systematic voltage sag frequency estimation method, considering the fault distribution density, fault ride-through (FRT) process of wind turbines during voltage sag and the interval characteristic of voltage sag frequency. First, this study proposes a fault distribution estimation model based on adaptive kernel density. Second, this study proposes a method for calculating the residual voltage and duration of voltage sag during FRT and combines the common distance protection action to analyse the effect on voltage sag by FRT process of wind turbines. Lastly, this study proposes an interval-valued voltage sag estimation method considering the interval characteristics of fault rate in the power system. IEEE 30-bus test system is used to verify the proposed method, the estimation results show better performance of the proposed method compared with the typical estimation methods.
Recently, it was found that most multiobjective particle swarm optimizers (MOPSOs) perform poorly when tackling many-objective optimization problems (MaOPs). This is mainly because the loss of ...selection pressure that occurs when updating the swarm. The number of nondominated individuals is substantially increased and the diversity maintenance mechanisms in MOPSOs always guide the particles to explore sparse regions of the search space. This behavior results in the final solutions being distributed loosely in objective space, but far away from the true Pareto-optimal front. To avoid the above scenario, this paper presents a balanceable fitness estimation method and a novel velocity update equation, to compose a novel MOPSO (NMPSO), which is shown to be more effective to tackle MaOPs. Moreover, an evolutionary search is further run on the external archive in order to provide another search pattern for evolution. The DTLZ and WFG test suites with 4-10 objectives are used to assess the performance of NMPSO. Our experiments indicate that NMPSO has superior performance over four current MOPSOs, and over four competitive multiobjective evolutionary algorithms (SPEA2-SDE, NSGA-III, MOEA/DD, and SRA), when solving most of the test problems adopted.
The paper shows how to estimate the three parameters of the generalized exponential Rayleigh distribution by utilizing the three estimation methods, namely, the moment employing estimation ...method (MEM), ordinary least squares estimation method (OLSEM), and maximum entropy estimation method (MEEM). The simulation technique is used for all these estimation methods to find the parameters for the generalized exponential Rayleigh distribution. In order to find the best method, we use the mean squares error criterion. Finally, in order to extract the experimental results, one of object oriented programming languages visual basic. net was used
•This paper proposes a robust modified perturb and observe (MPO) MPPT algorithm.•The proposed algorithm divides the P-V curve into four areas based on open-circuit voltage.•An efficient open-circuit ...voltage estimation is used to reduce required sensors number.•The proposed algorithm concentrates the search area of P-V curve to 15% of its region.•The proposed algorithm boosts the PV system tracking efficiency to 99.7%.
Considering the vast improvement of photovoltaics (PVs) efficiency, this paper proposes a robust modified perturb and observe (MPO) maximum power point tracking (MPPT) algorithm. The control strategy of the proposed MPO-MPPT algorithm is based on dividing the solar module/cell P-V curve into four operating areas depending on the open-circuit voltage estimation method. The two areas, which are located far from the maximum power point (MPP), utilize large voltage fixed step-size that improves the tracking speed of the PV system. Otherwise, a small step-size P&O MPPT algorithm is used to minimize the steady-state oscillations for the other two areas due to the close locations of these areas to the MPP. As well, the proposed tracking algorithm eliminates the necessity of wide small step-size iterations by concentrating small step-size search area to 15% of the whole P-V operation region. To prove the effectiveness of the proposed MPO algorithm compared with the conventional MPPT techniques; sinusoidal, ramp, and one-day (10 hr.) irradiance profiles are applied to the solar PV system. The proposed MPO-MPPT algorithm based solar PV system has been built using MATLAB/SIMULINK software. Obviously, the system results confirm the theoretical analysis of the proposed algorithm, which boosts the PV system tracking efficiency to 99.7%.
The accurate estimation of power system states is crucial for effective monitoring and control. However, the performance of conventional state estimators, which assume Gaussian measurement noise and ...do not account for denial-of-service attacks, can deteriorate significantly in real power systems. To address these issues, this paper proposes a novel robust state estimation method based on the quadratic function (QF) and the generalized correntropy loss function (GCL). The proposed QF-GCL state estimation method can effectively deal with non-Gaussian measurement noise and denial-of-service attacks. To enhance the computational efficiency, an influence function based solving method is developed. To determine the optimal parameters for the proposed QF-GCL state estimation method, a new state estimation error covariance equation is further derived. Simulations are performed on the IEEE 30-bus, 118-bus and 300-bus systems, to demonstrate the accurate and robust performance of the proposed QF-GCL robust state estimation method.
•A QF-GCL method is proposed.•An IF based solving method is given.•An error covariance is derived.
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