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Load curve data from advanced metering infrastructure record the consumers’ behavior. User consumption models help one understand a more intelligent power provisioning and clustering the load data is one of the popular approaches for building these models. Similarity measurements are important in the clustering model, but, load curve data is a time series style data, and traditional measurement methods are not suitable for load curve data. To cluster the load curve data more accurately, this paper applied an enhanced Pearson similarity for load curve data clustering. Our method introduces the ‘trend alteration point’ concept and integrates it with the Pearson similarity. By introducing a weight for Pearson distance, this method helps to keep the whole contour of the load data and the partial similarity. Based on the weighed Pearson distance, a weighed Pearson-based hierarchy clustering algorithm is proposed. Years of load curve data are used for evaluation. Several user consumption models are found and analyzed. Results show that the proposed method improves the accuracy of load data clustering.

To meet the demand of multiple power requirements, and enhance power utilization, a new type of dual-element electricity unit is designed in this study, which is a permanent magnet piston mechanical electric engine. Based on the analysis method of traditional internal combustion engines and linear generators, the working principle of the engine and the magnetic field distribution in the electrodynamic structure are analyzed, the machine dynamics model and electrodynamics model of the engine are established, then the theoretical evaluation is additionally established using finite elements. Based on this, an optimization model is constructed with the electrodynamic shape dimension as the optimization variable, with the intention of growing the output power. The optimization of the engine electrodynamic shape is executed via the use of the finite aspect approach and the NLPQL optimization algorithm integrated. The results show that the optimized engine output electricity expanded to 8.40 w, which is 18.81% greater than before optimization. An experimental prototype is developed, and the output voltage of the prototype is measured to verify the precept and overall performance of the new structure.

Breakdown (BD) voltage is significant in high-voltage power electric machines. Currently, BD voltages are mainly predicted by the semi-empirical formula in strongly inhomogeneous electric fields. However, the equation could not be applied for electrodes with weakly inhomogeneous electric fields. In this paper, positive lightning impulse BD voltages are predicted in various sphere-to-plane air gaps using forms of machine learning such as support vector regression (SVR), Bayesian regression (BR) and multilayer perceptron (MLP). Unlike previous studies, a method is also proposed by introducing streamer propagation characteristics as new features and by removing electric field gradients as unnecessary features to find out how to reduce the feature dimension. The streamer propagation characteristics are suggested to reflect the possibility of a discharge process between electrodes. Predicted voltages from machine learning algorithms are compared with the experimental results and calculated voltages from the semi-empirical formula. Firstly, the predictions from each model agreed well with the datasets. New features were observed to be applied for machine learning algorithms and to be as important as known electrostatic features before discharge. Secondly, predicted BD voltages were more accurate than calculated voltages from the semi-empirical equation in strongly inhomogeneous electric fields. Predictions from each model also agreed well with the experimental results in weakly inhomogeneous electric fields. The prediction accuracy of SVR was better than those of BR and MLP. Machine learning algorithms were also shown to be applied for electrodes with a wide range of inhomogeneities, unlike a semi-empirical method. We expect that the suggested features and machine learning algorithms can be used for accurately calculating BD voltages.

The exponential growth of unpredictable renewable power production sources in the power grid results in difficult-to-regulate reactive power. The ultimate goal of optimal reactive power dispatch (ORPD) is to find the optimal voltage level of all the generators, the transformer tap ratio, and the MVAR injection of shunt VAR compensators (SVC). More realistically, the ORPD problem is a nonlinear multi-objective optimization problem. Therefore, in this paper, the multi-objective ORPD problem is formulated and solved considering the simultaneous minimization of the active power loss, voltage deviation, emission, and the operating cost of renewable and thermal generators. Usually, renewable power generators such as wind and solar are uncertain; therefore, Weibull and lognormal probability distribution functions are considered to model wind and solar power, respectively. Due to the unavailability and uncertainty of wind and solar power, appropriate PDFs have been used to generate 1000 scenarios with the help of Monte Carlo simulation techniques. Practically, it is not possible to solve the problem considering all the scenarios. Therefore, the scenario reduction technique based on the distance metric is applied to select the 24 representative scenarios to reduce the size of the problem. Moreover, the efficient non-dominated sorting genetic algorithm II-based bidirectional co-evolutionary algorithm (BiCo), along with the constraint domination principle, is adopted to solve the multi-objective ORPD problem. Furthermore, a modified IEEE standard 30-bus system is employed to show the performance and superiority of the proposed algorithm. Simulation results indicate that the proposed algorithm finds uniformly distributed and near-global final non-dominated solutions compared to the recently available state-of-the-art multi-objective algorithms in the literature.

This paper introduces a framework for optimal placement (OP) of phasor measurement units (PMUs) using metaheuristic algorithms in a distribution network. The voltage magnitude and phase angle obtained from PMUs were selected as the input variables for supervised learning-based pseudo-measurement modeling that outputs the voltage magnitude and phase angle of the unmeasured buses. For three, four, and five PMU installations, the metaheuristic algorithms explored 2000 combinations, corresponding to 40.32%, 5.56%, and 0.99% of all placement combinations in the 33-bus system and 3.99%, 0.25%, and 0.02% in the 69-bus system, respectively. Two metaheuristic algorithms, a genetic algorithm and particle swarm optimization, were applied; the results of the techniques were compared to random search and brute-force algorithms. Subsequently, the effects of pseudo-measurements based on optimal PMU placement were verified by state estimation. The state estimation results were compared among the pseudo-measurements generated by the optimal PMU placement, worst PMU placement, and load profile (LP). State estimation results based on OP were superior to those of LP-based pseudo-measurements. However, when pseudo-measurements based on the worst placement were used as state variables, the results were inferior to those obtained using the LP.

This paper presents the optimal scheduling of a diesel generator and an energy storage system (ESS) while using a detailed battery ESS energy efficiency model. Optimal scheduling has been hampered to date by the nonlinearity and complexity of the battery ESS. This is due to the battery ESS efficiency being a multiplication of inverter and battery efficiency and the dependency of an inverter and any associated battery efficiencies on load and charging and discharging. We propose a combined mixed-integer linear programming and particle swarm optimization (MILP-PSO) algorithm as a novel means of addressing these considerations. In the algorithm, MILP is used to find some initial points of PSO, so that it can find better solution. Moreover, some additional algorithms are added into PSO to modify and, hence, improve its ability of dealing with constraint conditions and the local minimum problem. The simulation results show that the proposed algorithm performs better than MILP and PSO alone for the practical microgrid. The results also indicated that simplification or neglect of ESS efficiency when applying MILP to scheduling may cause a constraint violation.

Variable-speed wind turbines (VSWTs) typically use a maximum power-point tracking (MPPT) method to optimize wind-energy acquisition. MPPT can be implemented by regulating the rotor speed or by adjusting the active power. The former, termed speed-control mode (SCM), employs a speed controller to regulate the rotor, while the latter, termed power-control mode (PCM), uses an active power controller to optimize the power. They are fundamentally equivalent; however, since they use a different controller at the outer control loop of the machine-side converter (MSC) controller, the time dependence of the control system differs depending on whether SCM or PCM is used. We have compared and analyzed the power quality and the power coefficient when these two different control modes were used in fluctuating wind speeds through computer simulations. The contrast between the two methods was larger when the wind-speed fluctuations were greater. Furthermore, we found that SCM was preferable to PCM in terms of the power coefficient, but PCM was superior in terms of power quality and system stability.

This paper presents a decentralized control method for distributed generations (DGs) in an islanded direct current (DC) microgrid. In most typical DC microgrids, a decentralized control method is based on a voltage droop control method. However, the grid voltage differs from node to node due to line voltage drop, and hence the power sharing ratio among DGs cannot be matched with as desired value. Especially in an islanded DC microgrid including an energy storage system as a voltage source, it is difficult for DGs to maintain the charge state of the ESS in a decentralized way. To overcome this problem, state of charge (SOC)-voltage droop control is applied to the ESS. By using the proposed droop method, the SOC information can be assigned to the grid voltage, and hence the other DGs are able to support the SOC in a decentralized way. For DGs to enhance the accuracy of the SOC estimation, voltage drop is compensated for based on forecasting data and line impedance data. The simulation is modeled and implemented using Power System Computer Aided Design/Electromagnetic Transients for DC (PSCAD/EMTDC, version 4.2, Winnipeg, Manitoba, Canada) and the simulation results show that the capability to maintain SOC as well as the system voltage profile are improved by using the proposed method.

International Electronical Committee (IEC) 61850-90-7 is a part of the IEC 61850 series which specifies the advanced functions and object models for power converter based Distributed Energy Resources (DERs). One of its functions, the Voltage/VAR (V/V) control function, is used to enhance the stability and the reliability of the voltage in the distribution system. The conventional V/V function acts mainly for flattening the voltage profile as for a basic grid support function. Currently, other objectives such as the minimization of line loss and the operational costs reduction are coming into the spotlight. In order to attain these objectives, the V/V function and hence the DER units shall actively respond to the change of distribution system conditions. In this paper, the modification of V/V function and new requirements are proposed. To derive new requirements of V/V function, loss minimization is applied to a particle swarm optimization (PSO) algorithm where the condition of voltage constraint is considered not to deteriorate the voltage stability of the distribution system.

Standalone microgrids, which are mainly constructed on island areas have low system inertia, may result large frequency deviations even for small load change. Moreover, increasing penetration level of renewable energy sources (RESs) into standalone microgrids makes the frequency stability problem even worse. To overcome this problem, this paper proposes an active power sharing method with zero frequency deviations. To this end, a battery energy storage system (BESS) is operated as constant frequency (CF) control mode, whereas the other distributed generations (DGs) are operated as an active and reactive power (PQ) control mode. As a result, a state of charge (SOC) of the BESS is changed as the system load varies. Based on the SOC deviation, DGs share the load change. The SOC data is assumed to be sent via communication system, hence the communication time delay is considered. To enhance reliability, controllers of DGs are designed to take account of the failure of communication system. The simulation results show that active power can be shared among DGs according to desired ratio without frequency deviations even for large variation of output power of RESs.