A Hybrid Signal Processing Method Combining Mathematical Morphology and Walsh Theory for Power Quality Disturbance Detection and Classification

In this paper, a novel signal processing method combining mathematical morphology (MM) and Walsh theory is proposed, which uses Walsh functions to control the structuring element (SE) and MM operators. Based on the Walsh-MM method, a scheme for power quality disturbances detection and classification is developed, which involves three steps: denoising, feature extraction and morphological clustering. First, various evolution rules of Walsh function are used to generate groups of SEs for the multiscale Walsh-ordered morphological operation, so the original signal can be denoised. Next, the fundamental wave of the denoised signal is suppressed by Hadamard matrix;thus, disturbances can be extracted. Finally, the Walsh power spectrum of the waveform extracted in the previous step is calculated, and the parameters of which are taken by morphological clustering to classify the disturbances. Simulation results reveal the proposed scheme can effectively detect and classify disturbances, and the Walsh-MM method is less affected by noise and only involves simple calculation, which has a potential to be implemented in hardware and more suitable for real-time application.

Transitional Revenue Scheme for High-cost Generator Unit in Guangdong Electricity Market Based on Government Authorized Contracts

High-cost generator units are at a cost disadvantage in electricity spot markets.This study focuses on revenue mechanisms of gas-fired units affected by power market reform in Guangdong province,China.For the first time,we compare impacts on market indicators of five settlement mechanisms:feed-in tariff(FiT),location marginal price(LMP),contract for difference(CFD),direct subsidy(DS),and estimated revenue method(ERM).In particular,we design key parameters,including authorized CFD and ERM of two kinds of government authorized contracts based on traditional dispatching patterns to avoid significant profit fluctuations brought by market reforms.We also analyze impact of factors such as climbing performance,seasonal load,and subsidy amount on the overall market and its players.Results of a case study show to directly subsidize gasfired units will lead higher-cost units to generate more electricity,with a resulting loss of social welfare.Disruption of market prices and provision of unreasonable incentives are fatal disadvantages of this subsidy method.The government and policymakers should consider financial means to adjust benefits to reduce production costs and increase social welfare.Also,by case analysis,the ERM shows its stable performance in revenue of high-cost units,while we find that authorized CFD is not applicable for gas-fired units whose output is unstable as a marginal unit frequently.Therefore,we suggest government agencies adopt ERM to sign contracts with gas-fired units,to attain a balance between fairness and efficiency.

Probabilistic Optimal Power Flow Considering the Dependence of Multiple Wind Farms Using Pair Diffusive Kernel Copula

As wind farms are commonly installed in areas with abundant wind resources,spatial dependence of wind speed among nearby wind farms should be considered when modeling a power system with large-scale wind power.In this paper,a novel bivariate non-parametric copula,and a bivariate diffusive kernel(BDK)copula are proposed to formulate the dependence between random variables.BDK copula is then applied to higher dimension using the pair-copula method and is named as pair diffusive kernel(PDK)copula,offering flexibility to formulate the complicated dependent structure of multiple random variables.Also,a quasi-Monte Carlo method is elaborated in the sampling procedure based on the combination of the Sobol sequence and the Rosen-blatt transformation of the PDK copula,to generate correlated wind speed samples.The proposed method is applied to solve probabilistic optimal power flow(POPF)problems.The effectiveness of the BDK copula is validated in copula definitions.Then,three different data sets are used in various goodness-of-fit tests to verify the superior performance of the PDK copula,which facilitates in formulating the dependence structure of wind speeds at different wind farms.Furthermore,samples obtained from the PDK copula are used to solve POPF problems,which are modeled on three modified IEEE 57-bus power systems.Compared to the Gaussian,T,and parametric-pair copulas,the results obtained from the PDK copula are superior in formulating the complicated dependence,thus solving POPF problems.

Impact of Long-Term Transactions on Strategic Bidding in the Electricity Market

Power producers'profits are determined by the market price in the electricity market and therefore they will adopt certain strategies in market transactions to achieve higher profits.In an electricity spot market that adopts uniform pricing,power producers with considerable generation capacity are able to exercise their market power,given that the market concentration is relatively high at the beginning of market reform.It has been proved that an effective bidding strategy can increase the market clearing prices,so as to increase the profits of the power producer.Fortunately,the introduction of long-term transactions may mitigate the impact of producers'market power,as a great amount of long-term volume is settled at the long-term contract price,which is determined in advance and is less fluctuating than the spot price.Two forms of long-term transactions,the fixed volume contract and the varied volume contract,are studied in this paper.Simulation studies are conducted on a multi-agent platform,where both the long-term and spot transactions of power producers are included,and the total profits of a power producer with or without long-term transactions are analyzed to demonstrate their influence.Meanwhile,the results clearly show that long-term transactions can effectively prevent power producers from exercising market power.

Coordinated optimal dispatch and market equilibrium of integrated electric power and natural gas networks with P2G embedded

As power to gas(P2 G) technology gradually matures, the coupling between electricity networks and natural gas networks should ideally evolve synergistically.With the intent of characterizing market behaviors of integrated electric power and natural gas networks(IPGNs)with P2 G facilities, this paper establishes a steady-state model of P2 G and constructs optimal dispatch models of an electricity network and a natural gas network separately. In addition, a concept of slack energy flow(SEF) is proposed as a tool for coordinated optimal dispatch between the two networks. To study how the market pricing mechanism affects coordinated optimal dispatch in an IPGN, a market equilibrium-solving model for an IPGN is constructed according to game theory, with a solution based on the Nikaido-Isoda function. Case studies are conducted on a joint model that combines the modified IEEE 118-node electricity network and the Belgian 20-node gas network.The results show that if the game between an electric power company and a natural gas company reaches market equilibrium, not only can both companies maximize their profits, but also the coordinated operation of the coupling units, i.e., gas turbines and P2 G facilities, will contribute more to renewable energy utilization and carbon emission reduction.

Stochastic Dynamic Economic Dispatch of Wind-integrated Electricity and Natural Gas Systems Considering Security Risk Constraints

As the proportion of wind power generation increases in power systems,it is necessary to develop new ways for wind power accommodation and improve the existing power dispatch model.The power-to-gas technology,which offers a new approach to accommodate surplus wind power,is an excellent way to solve the former.Hence,this paper proposes to involve power-to-gas technology in the integrated electricity and natural gas systems(IEGSs).To solve the latter,on one hand,a new indicator,the scale factor of wind power integration,is introduced into the wind power stochastic model to better describe the uncertainty of grid-connected wind power;on the other hand,for quantizing and minimizing the impact of the uncertainties of wind power and system loads on system security,security risk constraints are established for the IEGS by the conditional value-at-risk method.By considering these two aspects,an MILP formulation of a security-risk based stochastic dynamic economic dispatch model for an IEGS is established,and GUROBI obtained from GAMS is used for the solution.Case studies are conducted on an IEGS consisting of a modified IEEE 39-bus system and the Belgium 20-node natural gas system to examine the effectiveness of the proposed dispatch model.

Short-term local prediction of wind speed and wind power based on singular spectrum analysis and locality-sensitive hashing

With the growing penetration of wind power in power systems, more accurate prediction of wind speed and wind power is required for real-time scheduling and operation. In this paper, a novel forecast model for shortterm prediction of wind speed and wind power is proposed,which is based on singular spectrum analysis(SSA) and locality-sensitive hashing(LSH). To deal with the impact of high volatility of the original time series, SSA is applied to decompose it into two components: the mean trend,which represents the mean tendency of the original time series, and the fluctuation component, which reveals the stochastic characteristics. Both components are reconstructed in a phase space to obtain mean trend segments and fluctuation component segments. After that, LSH is utilized to select similar segments of the mean trend segments, which are then employed in local forecasting, so that the accuracy and efficiency of prediction can be enhanced. Finally, support vector regression is adopted forprediction, where the training input is the synthesis of the similar mean trend segments and the corresponding fluctuation component segments. Simulation studies are conducted on wind speed and wind power time series from four databases, and the final results demonstrate that the proposed model is more accurate and stable in comparison with other models.

Multi-mapping Fault Diagnosis of High Voltage Circuit Breaker Based on Mathematical Morphology and Wavelet Entropy

Mechanical faults of high voltage circuit breakers(CBs)seriously affect the reliability of their operation,which may cause severe damage to power systems.In order to monitor operational conditions and detect mechanical faults of CBs,a multi-parameter monitoring system is designed and a fault diagnosis method based on multi-mapping is proposed.The paper focuses on the trip/close circuits,the spring-charging mechanism and the transmission mechanism,and obtains four current signals and a vibration signal that can reflect CB conditions.For the current signals,a morphological filter is used to remove noise and then characteristics of the waveforms’shape information are extracted.For vibration signals,the wavelet packet transform is used to decompose the signal into various frequency bands,and the sample entropy of the low frequency bands and the wavelet energy of the high frequency bands are calculated,respectively.Based on these feature parameters,a multi-mapping strategy is proposed for CB fault diagnosis.Laboratory experiments have been conducted to obtain on-site signals under various conditions,and experiment results have verified that monitoring the aforementioned signals and using the corresponding feature extraction and fault diagnosis methods,the mechanical faults of high voltage CBs can be effectively diagnosed.

Fault Identification in Power Network Based on Deep Reinforcement Learning

With the integration of alternative energy and renewables,the issue of stability and resilience of the power network has received considerable attention.The basic necessity for fault diagnosis and isolation is fault identification and location.The conventional intelligent fault identification method needs supervision,manual labelling of characteristics,and requires large amounts of labelled data.To enhance the ability of intelligent methods and get rid of the dependence on a large amount of labelled data,a novel fault identification method based on deep reinforcement learning(DRL),which has not received enough attention in the field of fault identification,is investigated in this paper.The proposed method uses different faults as parameters of the model to expand the scope of fault identification.In addition,the DRL algorithm can intelligently modify the fault parameters according to the observations obtained from the power network environment,rather than requiring manual and mechanical tuning of parameters.The methodology was tested on the IEEE 14 bus for several scenarios and the performance of the proposed method was compared with that of population-based optimization methods and supervised learning methods.The obtained results have confirmed the feasibility and effectiveness of the proposed method.

Ultra-short Term Wind Speed Prediction Using Mathematical Morphology Decomposition and Long Short-term Memory

This paper proposes a new model,which consists of a mathematical morphology(MM)decomposer and two long short term memory(LSTM)networks,to perform ultra-short term wind speed forecast.The MM decomposer is developed in order to improve the forecast accuracy,which separates the wind speed into two parts:a stationary long-term baseline and a nonstationary short-term residue.Afterwards,two LSTM networks are implemented to forecast the baseline and residue,respectively.Besides,this paper makes an integrated forecast that takes into account multiple climate factors,such as temperature and air pressure.The baseline,temperature and air pressure are used as the inputs of baseline network for training and prediction,and the baseline,residue,temperature and air pressure are used as the inputs of residue network for training and prediction.The performance of the proposed model has been validated using data collected from the Australian Meteorological Station,which is compared with least squares-support vector machine(LS-SVM),back-propagation artificial neural network(BPNN),LSTM,MM-LS-SVM,and MM-BPNN.The results demonstrate that the proposed model is more suitable to solve non-stationary time-series forecast,and achieves higher accuracy than the other models under various conditions.

Fault Location in Distribution System Using Convolutional Neural Network Based on Domain Transformation

Distribution lines are integral parts of the modern power system,which can affect the security and stability of power supply directly.An effective power system protection scheme should be able to detect all occurring faults as soon as possible.There are two tasks in fault diagnosis.One is the fault classification,where high accuracy rates have already achieved.Thus,this paper focuses on the other task,i.e.fault location.Enlightened by Fourier transform,this paper proposes an online data-driven method,which transforms signals from time domain to image domain through signal-to-image(SIG)algorithm and then process the transformed images with framework based on convolutional neural network(CNN).On the one hand,we can extract more crucial characteristic and information from image domain.On the other hand,the CNN-based structure is much smaller than others.It needs less memory space and would be easier to be transplanted to hardware platform.Moreover,the proposed algorithm does not require synchronous devices.The numerical comparison shows that the proposed SIG-CNN fault location model achieves robust and accurate results compared with other data-driven algorithms.

Application of Morphological Max-Lifting Scheme for Identification of Induction Motor Stator Inter-turn Short Circuit

The harmonic components of stator winding current in induction motor will change under the condition of stator inter-turn short circuit.According to these characteristics,in this paper,a novel technique based on morphological maxlifting scheme is proposed for identification of induction motor stator inter-turn short circuit.A max-lifting scheme is applied to process stator winding currents to extract these characteristics.An indicator,r,is computed to identify the short circuit.The transient model of induction motor is employed to simulate oneturn to six-turn stator inter-turn short circuits in an induction motor.Extensive simulation work has been conducted under normal conditions,abnormal conditions(voltage imbalance and varying load),stator inter-turn short circuit conditions,and conditions of any combinations of the above.The results have shown that the scheme proposed in this paper has a high identification rate for induction motor stator inter-turn short circuit.

Selective Learning for Strategic Bidding in Uniform Pricing Electricity Spot Market

In an electricity market,power producers’profits are determined by the market price instead of the regulated price.Therefore,the producers should be cautious in strategic bidding,for which prediction-based approaches are widely used and have been proved effective for many areas.However,in a uniform pricing market,the market environment is so complicated,which is primarily due to the complexity of the participants’interaction,that even the strategies based on machine learning algorithms,which are generally considered as outstanding nonlinear prediction methods,may sometimes lead to unsatisfactory results.Therefore,a selective learning scheme for strategic bidding is proposed to ensure greater effectiveness.The proposed scheme is based on an ensemble technique,where several machine learning algorithms serve as the underlying algorithms to predict the price and generate a bidding recommendation.As the clearing iteration progresses,the most fitting ones will be chosen to dominate the bidding strategy.Considering the characteristics of the electricity market,the prediction method used in the selective learning scheme is modified to achieve higher accuracy.Simulation studies are presented to demonstrate the effectiveness of the proposed scheme,which leads to more reasonable bidding behaviors and higher profits.

Stochastic Economic Dispatch Considering the Dependence of Multiple Wind Farms Using Multivariate Gaussian Kernel Copula

Wind farms usually cluster in areas with abundant wind resources.Therefore,spatial dependence of wind speeds among nearby wind farms should be taken into account when modeling a power system with large-scale wind power penetration.This paper proposes a novel non-parametric copula method,multivariate Gaussian kernel copula(MGKC),to describe the dependence structure of wind speeds among multiple wind farms.Wind speed scenarios considering the dependence among different wind farms are sampled from the MGKC by the quasi-Monte Carlo(QMC)method,so as to solve the stochastic economic dispatch(SED)problem,for which an improved meanvariance(MV)model is established,which targets at minimizing the expectation and risk of fuel cost simultaneously.In this model,confidence interval is applied in the wind speed to obtain more practical dispatch solutions by excluding extreme scenarios,for which the quantile-copula is proposed to construct the confidence interval constraint.Simulation studies are carried out on a modified IEEE 30-bus power system with wind farms integrated in two areas,and the results prove the superiority of the MGKC in formulating the dependence among different wind farms and the superiority of the improved MV model based on quantilecopula in determining a better dispatch solution.