A novel intelligent approach for yaw position forecasting in wind energy systems

Yaw control systems orientate the rotor of a wind turbine into the wind direction, optimize the wind power generated by wind turbines and alleviate the mechanical stresses on a wind turbine. Regarding the advantages of yaw control systems, a k-nearest neighbor classifier (k-NN) has been developed in order to forecast the yaw position parameter at 10-min intervals in this study. Air temperature, atmosphere pressure, wind direction, wind speed, rotor speed and wind power parameters are used in 2, 3, 4, 5 and 6-dimensional input spaces. The forecasting model using Manhattan distance metric for k = 3 uncovered the most accurate performance for atmosphere pressure, wind direction, wind speed and rotor speed inputs. However, the forecasting model using Euclidean distance metric for k = 1 brought out the most inconsistent results for atmosphere pressure and wind speed inputs. As a result of multi-tupled analyses, many feasible inferences were achieved for yaw position control systems. In addition, the yaw position forecasting model developed was compared with the persistence model and it surpassed the persistence model significantly in terms of the improvement percent.     

Stochastic mid-term generation scheduling incorporated with wind power

A challenge now facing system operator is how to schedule optimally the generation units in a wind integrated power system over a one year time horizon considering the effects of wind forecasting and variability; also, regarding the effects of load uncertainty. By the same token, this paper first develops a new formulation for Stochastic Mid-term Generation Scheduling (SMGS). In the formulation, 2m + 1 point estimate method is developed to accurately estimate the output variables of Mid-term Generation Scheduling (MGS) problem. Then, the formulation is combined with adaptive modified gravitational search algorithm and a novel self-adaptive wavelet mutation strategy for the establishment of new robust algorithm for the present problem. It is noteworthy to say that the classical methods considered certain wind information in the deterministic solution of the MGS problem which is not the realistic approach. However, this study improves modeling of wind–thermal system in the MGS problem by considering possible uncertainties when scheduling the generators of power system. The proposed model is capable of taking uncertainty of load and wind into account. The proposed method is applied on two test cases and the numerical results confirmed the efficiency and stability of the proposed algorithm.     

Pole-zero assignment adaptive stabiliser

This paper presents a new adaptive power system stabiliser able to provide acceptable damping over a wide range of operating points. The control strategy is based on a new adaptive technique named Pole-Zero Assignment Controller (PZAC) in which a particular power system transfer function (G_d(s) = Δδ/ΔP_m) is modified to a standard form based on an explicit system identification. Controller design is mainly based on continuous-time system because of using the delta operator rather than the more usual shift operator. Simulation studies performed on a multimachine model are presented. Results clearly show the benefits of the proposed adaptive controller for stability enhancement of a power system, especially where there are large changes in operating point.     

Model of distribution system total supply capability considering feeder and substation transformer contingencies

This paper proposes a model for calculating the total supply capability (TSC) for distribution system considering both feeder and substation transformer contingencies. Existing models and methods for TSC only consider substation transformer contingencies and ignore feeder contingencies. However, the feeder contingencies occur much more frequently than substation transformer contingencies in practice. Moreover, some operation state fail the feeder contingencies N − 1 verification even they pass the transformer contingencies N − 1 verification. In this paper, a TSC model is firstly proposed in which feeder and transformer N − 1 contingencies are fully considered. This model is designed in feeder level, which means the topology of interconnection among feeders is accurately modeled. Secondly, a supplementary model for load balancing is set up for a better load distribution solution on feeders and transformers at TSC loading. Finally, the method is tested in a test distribution system and a real partial distribution network and the results are verified by the traditional N − 1 simulation.     

Optimal design of a robust discrete parallel FP + FI + FD controller for the Automatic Voltage Regulator system

The purpose of this paper is to design a good tracking controller for the generator Automatic Voltage Regulator (AVR) system. A fuzzy logic-based controller that is called Fuzzy P + Fuzzy I + Fuzzy D (FP + FI + FD) controller has been designed optimally and applied to AVR system. In the proposed method, optimal tuning of controller parameters is very important to achieve the desired level of robust performance. Thus, a hybrid of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) (HGAPSO) technique has been used to find a better fuzzy system control. The motivation for using this hybrid method is to increase disturbance rejection effort, reduce fuzzy system efforts and take large parametric uncertainties into account. The developed FP + FI + FD control strategy leads to a flexible controller with simple structure that is easy to implement. The simulation results have been compared with the conventional Proportional–Integral–Derivative (PID) and fuzzy PID controllers. Three cases of simulation have been performed, case 1: comparing the tracking capability of the controllers, case 2: comparing the disturbance rejection capability of the controller and case 3: evaluating the performance of the controllers assuming that amplifier and exciter system parameters have 50% uncertainty. The simulation results shows that the proposed parallel FP + FI + FD controller has good performance from the perspective of overshoot/undershoot, settling time, and rise time in comparison with both conventional and fuzzy PID controllers.     

Energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle based on fuzzy logic

In order to enhance the fuel economy of hybrid vehicle and increase the mileage of continuation of journey, a fuzzy logic control is utilized to design energy management strategies for fuel cell/battery (FC + B) hybrid vehicle and fuel cell/battery/ultra-capacitor (FC + B + UC) hybrid vehicle. The models of hybrid vehicle for FC + B and FC + B + UC structure are developed by electric vehicle simulation software ADVISOR which uses a hybrid backward/forward approach. The results demonstrate that the proposed control strategy can satisfy the power requirement for four standard driving cycles and achieve the power distribution among various power sources. The comprehensive comparisons with the power tracking control strategy which is wide adopted in ADVISOR verify that the proposed control strategy has better rationality and validity in terms of fuel economy and dynamic property in four standard driving cycles. Therefore, the proposed strategy will provide a novel approach for the advanced energy management system of hybrid vehicle.     

A hybrid economic indices based short-term load forecasting system

The bankruptcy event of Lehman Brothers and the corresponding global economic recession in 2008 and 2009, influenced the electricity load demand patterns for which traditional load forecasting approaches were not able to effectively predict. To overcome this problem, this paper proposes a new hybrid economic indices based short-term load forecasting (HEI-STLF) system. In which business indicators, such as the leading index or the coincide index, each combined with stock index as hybrid economic indices influencing factors for the support vector regression (SVR) model, to respond to the economic dynamics and reduce its impact on forecasting accuracy. The Taiwan island-wide electricity load demands from 2008 to 2011 are used as the case study for performance testing with different combinations of the Taiwan business indicator and the Taiwan Stock Exchange Capitalization-Weighted Stock Index (TAIEX). The results show that the proposed HEI-STLF system with hybrid economic indices of an annualized six-month rate of change of composite leading index and a 90 days moving average of TAIEX, achieves the best forecasting performance. Compared to the traditional SVR load forecasting approach, it improves the forecasting accuracy in the best condition by 30.39% in the period when the load demands are affected by the global economic recession.     

An online power system static security assessment module using multi-layer perceptron and radial basis function network

Efficient contingency screening and ranking method has gained importance in modern power systems for its secure operation. This paper proposes two artificial neural networks namely multi-layer feed forward neural network (MFNN) and radial basis function network (RBFN) to realize the online power system static security assessment (PSSSA) module. To assess the severity of the system, two indices have been used, namely active power performance index and voltage performance index, which are computed using Newton–Raphson load flow (NRLF) analysis for variable loading conditions under N − 1 line outage contingencies. The proposed MFNN and RBFN models based PSSSA module, are fed with power system operating states, load conditions and N − 1 line outage contingencies as input features to train the neural network models, to predict the performance indices for unseen network conditions and rank them in descending order based on performance indices for security assessment. The proposed approaches are tested on standard IEEE 30-bus test system, where the simulation results prove its performance and robustness for power system static security assessment. The comparison of severity obtained by the neural network models and the NRLF analysis in terms of time and accuracy, signifies that the proposed model is quick, accurate and robust for power system static security evaluation for unseen network conditions. Thus, the proposed PSSSA module implemented using MFNN and RBFN models are found to be feasible for online implementation.     

Effect of temperature on short term load forecasting using an integrated ANN

An integrated Artificial Neural Network (ANN) approach to Short-Term Load Forecasting (STLF) is proposed in this paper. Four modules consisting of the Basic ANN, Peak and Valley ANN, Averager and Forecaster and Adaptive Combiner form the integrated method for load forecasting. The Basic ANN uses the historical data of load and temperature to predict the next 24 h load, while the Peak and Valley ANN uses the past peak and valley data of load and temperatures, respectively. The Averager captures the average variation of the load from the previous load behaviour, while the adaptive combiner uses the weighted combination of outputs from the Basic ANN and the Forecaster, to forecast the final load. The regression based and time series methods are conceptually incorporated into the ANN to obtain an integrated load forecasting approach.     

Effect of varying concentration and temperature on steady and dynamic parameters of low concentration photovoltaic energy system

Low-concentration photovoltaic (LCPV) system has huge potential for further cost reduction of solar photovoltaic (PV) power as compared to flat panel PV. The dependence of steady state and dynamic parameters on concentration and temperature is crucial to extract maximum power from solar photovoltaic system. This article aims to present the effect of varying concentration and temperature on steady state and dynamic parameters of LCPV system under actual test conditions (ATC). The rate of change in I_SC with solar irradiation i.e., dI_SC/dG is found as 0.25 A/W assuming ≈±1 °C change in module temperature. The effect of temperature on inherent material properties responsible for photo-conversion efficiency is studied using impedance spectroscopy technique. A linear response of series resistance of LCPV module is observed with respect to change in module temperature, i.e. dR_S/dT from 297 to 333 K is in the range of 1.15–1.20 Ω with a rate of 1 mΩ/K. From real-time analysis of LCPV system open-circuit voltage found decreasing from 21 to 20.6 V with temperature coefficient of voltage ≈−0.061 V/K. The dynamic resistance has a positive coefficient of module temperature i.e., dr_d/dT given by 0.49 Ω/K.     

Improving electric load forecasts using network committees

Accurate daily peak load forecasts are important for secure and profitable operation of modern power utilities, with deregulation and competition demanding ever-increasing accuracies. Machine learning techniques including neural and abductive networks have been used for this purpose. Network committees have been proposed for improving regression and classification accuracy in many disciplines, but are yet to be widely applied to load forecasting. This paper presents a formal approach to apply the technique using historical load and temperature data spanning multiple years, with individual committee members trained on different years. Correlation among data for successive years is investigated and methods to enhance independence between member models for improving committee performance are described. Both neural and abductive networks implementations are presented and compared. An abductive network three-member committee was developed on data for three successive years and evaluated on the fourth year. Compared to a monolithic model trained on the same full three-year data, the committee reduces the mean absolute percentage error from 2.52% to 2.19%. The corresponding reduction in the mean of the absolute error from 70 MW to 61 MW is statistically significant at the 95% confidence level.     

Maclaurin–Newton algorithms in 132 kV subtransmission security simulations

This paper examines how applicable approximate Jacobian inversions are when implemented in the security analysis simulations of 132 kV power subtransmission. The complete Scottish 400/275/132 kV power transmission network was simulated, including the 132 kV subtransmission network with its high r/x ratios. Both the coupled and decoupled Maclaurin–Newton load flow algorithms were tested. It was proved that high r/x ratios found in the 132 kV level, five times higher than in 400/275 kV, have an important influence on convergence and accuracy of the inversion Jacobian load flow algorithms. It was found that the decoupled inversion load flow was applicable for 132 kV, it converged regularly, but had worse convergence and accuracy characteristics, compared to 400/275 kV applications, while the coupled inversion load flow was not applicable at all for 132 kV, it always diverged.     

Robust non-fragile power system stabilizer

This paper presents a step towards the design of robust non-fragile power system stabilizers (PSSs) for single-machine infinite-bus systems. To ensure resiliency of a robust PSS, the proposed approach presents a characterization of all stabilizers that can guarantee robust stability (RS) over wide range of operating conditions. A three-term controller (x₁ + x₂s)/(1 + x₃s) is considered to accomplish the design. Necessary and sufficient stability constraints for existing of such controller at certain operating point are derived via Routh–Hurwitz criterion. Continuous variation in the operating point is tackled by an interval plant model where RS problem is reduced to simultaneous stabilization of finite number of plants according to Kharitonov theorem. Controller triplets that can robustly stabilize vertex plants are characterized in a similar manner. The most resilient controller is computed at the center of maximum-area inscribed rectangle. Simulation results confirm robustness and resiliency of the proposed stabilizer.     

Experimental analysis and optimization of key parameters of ZVS mode and its application in the proposed LLC converter designed for distributed power system application

This paper deals with experimental analysis of zero-voltage switching mode targeting high-frequency operation of chosen MOSFET type. After selection of specific type of transistor (IPW60R165CP) the experimental investigation has been made by changing parameters (e.g. dead-time, auxiliary capacitance of MOSFET, transistor current), that are influencing the ZVS commutation process. For these purposes we constructed the universal testing device, which is capable to secure realistic conditions of various types of commutation modes (hard switching, zero-voltage switching, zero-current switching). Afterwards the best settings of commutation mode have been utilized in proposed LLC converter suited for distributed power system application. Prototype is operating in ZVS region with optimized parameters. Switching frequency is from 130 kHz (input voltage 325 Vdc) to 210 kHz (input voltage 415 Vdc) with the output power of 1500 W. It is clear from the results that experimental analysis of the ZVS commutation mode brings expectation of transistor behavior which was totally confirmed also in the case of experimental analysis of LLC resonant converter.     

Power and energy management of grid/PEMFC/battery/supercapacitor hybrid power sources for UPS applications

This paper presents a hybrid power and energy source supplied by a proton exchange membrane fuel cell (PEMFC) as the main power source in an uninterruptible power supply (UPS) system. To prevent the PEMFC from fuel starvation and degradation and realize their seamless linking in the hybrid UPS system, the power and energy are balanced by the battery and/or supercapacitor (SC) as two alternative auxiliary power sources. Based on the modeling and sizing of hybrid power and energy components, the power and energy management strategies and efficiency measurements of four operating modes in UPS system are proposed. To evaluate the proposed strategies, an experimental setup is implemented by a data acquisition system, a PEMFC generating system, and a UPS system including AC/DC rectifier, DC/AC inverter, DC/DC converter, AC/DC recharger and its intelligent control unit. Experimental results with the characteristics of a 300 W self-humidified air-breathing of PEMFC, 3-cell 12 V/5 Ah of batteries, and two 16-cell 120 F/2.7 V of SCs in parallel corroborate the excellent management strategies in the four operating modes of UPS system, which provides the basis for the optimal design of the UPS system with hybrid PEMFC/battery/SC power sources.     

Direct power control of a multilevel inverter based active power filter

A cascaded H-bridge multilevel inverter based active power filter with a novel direct power control is proposed in this paper. It can be directly connected to medium/high voltage power line without using the bulky transformer or passive filter. Due to the limited switching frequency (typically below 1 kHz) of high-power solid-state devices (GTO/IGCT), multiple synchronous/stationary reference frame current controllers are reviewed and derived. Based on this, a novel current controller is proposed for harmonic current elimination and system power factor compensation. Furthermore, a synchronous/stationary hybrid structure can be derived with fundamental de-coupling control. The instantaneous reactive power theory and synchronous reference frame based control are compared based on mathematical models. A direct power control concept is then derived and proposed. It is equivalent as the hybrid synchronous/stationary frame current controller, but has a simpler implementation. It has clear physical meaning and can be considered as a simplified version of the hybrid frame current controller. Simulations on a 4160 V/1.2 MVA system and experimental results on a 208 V/6 kVA laboratory prototype are presented to validate the proposed active power filter design.     

A time series spot price forecast model for the Nord Pool market

We present three relatively simple spot price forecast models for the Nord Pool market based on historic spot and futures prices including data for inflow and reservoir levels. The models achieve a relatively accurate forecast of the weekly spot prices. The composite regression model achieves a mean absolute percentage error (MAPE) of around 7.5% and under-forecasts the actual spot price by some 1.4 NOK/MW h in the sample period. Out of sample testing achieves a MAPE of around 7.4% including a match of the actual spot price. A myopic model using the previous week’s spot price as a predictor for the next week’s spot price achieves a MAPE of 7.5% and under-forecasts the actual spot price by some 0.9 EUR/MW h. A futures model using the futures price for next week as a predictor for next week’s spot price achieves a MAPE of 5.3% and over-forecast the actual spot price by some 4.3 EUR/MW h.     

Study on FRT compliance of VSC-HVDC connected offshore wind plants during AC faults including requirements for the negative sequence current control

In this paper three new control modules are introduced for offshore wind power plants with VSC-HVDC transmission. The goal is to enhance the Fault Ride Thought (FRT) capability of the HVDC system and the connected offshore wind power plant during balanced and unbalanced AC faults. Firstly, a positive-sequence-voltage-dependent (PSVD) active current reduction control loop is introduced to the offshore wind turbines. The method enhances the performance of the offshore AC voltage drop FRT compliance strategy. Secondly, an adaptive current limiting control strategy which operates simultaneously on the positive and the negative sequence current is discussed. It enables negative sequence current injection, while at the same time respecting the maximum fault current capacity of the HVDC converter station. Finally, a state machine is proposed for the VSC-HVDC system and for the offshore wind turbines respectively. It coordinates the fault and the post-fault response during balanced as well as unbalanced faults, ensuring a smooth shift from the normal operating point towards the fault and the post-fault period. The test system consists of a two level VSC-HVDC link, rated at ±250 kV, connecting an offshore wind power plant with 700 MW generation capacity. Simulation results with a detailed EMT type model in PSCAD/EMTDC environment are presented.     

Transmission congestion management in bilateral markets: An interruptible load auction solution

This paper demonstrates that appropriate invocation of interruptible loads by the independent system operator (ISO) can aid in relieving transmission congestion in power systems. An auction model is proposed, for an ISO operating in a bilateral contract dominated market, for real-time selection of interruptible load offers while satisfying the congestion management objective. The proposed congestion management scheme using interruptible loads can specifically identify load buses where corrective measures are needed for relieving congestion on a particular transmission corridor. The N − 1 contingency criterion has been taken into account to simulate various cases, and hence, examine the effectiveness of the proposed method. It has been shown that the method can assist the ISO to remove the overload from lines in both normal and contingency conditions in an optimal manner.     

Rank-constrained semidefinite program for unit commitment

Unit Commitment (UC) is a combinatorial optimization problem that can be posed as minimizing a quadratic objective function under quadratic constraints. This paper presents a solution to UC based on Semidefinite Programming (SDP). In particular, it shows that an approximate solution can be obtained by using Shor’s semidefinite relaxation scheme together with a rank constraint enforced via convex iteration. The approximate solution has the majority of Boolean variables set by the SDP solver to either 0 or 1; it is modified by a simple heuristic to yield a feasible schedule. The proposed SDP formulation employs 3 × 3 semidefinite matrices and therefore requires computational effort that increases only moderately with problem size. Numerical results on test systems with up to 100 units dispatched over a period of 24 h show that the method is robust and produces schedules that are comparable with those from previous techniques.