Power Information System Database Cache Model Based on Deep Machine Learning

At present,the database cache model of power information system has problems such as slow running speed and low database hit rate.To this end,this paper proposes a database cache model for power information systems based on deep machine learning.The caching model includes program caching,Structured Query Language(SQL)preprocessing,and core caching modules.Among them,the method to improve the efficiency of the statement is to adjust operations such as multi-table joins and replacement keywords in the SQL optimizer.Build predictive models using boosted regression trees in the core caching module.Generate a series of regression tree models using machine learning algorithms.Analyze the resource occupancy rate in the power information system to dynamically adjust the voting selection of the regression tree.At the same time,the voting threshold of the prediction model is dynamically adjusted.By analogy,the cache model is re-initialized.The experimental results show that the model has a good cache hit rate and cache efficiency,and can improve the data cache performance of the power information system.It has a high hit rate and short delay time,and always maintains a good hit rate even under different computer memory;at the same time,it only occupies less space and less CPU during actual operation,which is beneficial to power The information system operates efficiently and quickly.

Distributed Model Predictive Load Frequency Control of Multi-area Power System with DFIGs

Reliable load frequency control LFC is crucial to the operation and design of modern electric power systems. Considering the LFC problem of a four-Area interconnected power system with wind turbines, this paper presents a distributed model predictive control DMPC based on coordination scheme. The proposed algorithm solves a series of local optimization problems to minimize a performance objective for each control area. The generation rate constraints GRCs, load disturbance changes, and the wind speed constraints are considered. Furthermore, the DMPC algorithm may reduce the impact of the randomness and intermittence of wind turbine effectively. A performance comparison between the proposed controller with and without the participation of the wind turbines is carried out. Analysis and simulation results show possible improvements on closed-loop performance, and computational burden with the physical constraints. © 2014 Chinese Association of Automation.

Power System Aggregate Load Area Modelling by Particle Swarm Optimization

This paper presents a new approach for deriving a power system aggregate load area model (ALAM). In this approach, an equivalent area load model is derived to represent the load characters for a particular area load of a power system network. The Particle Swarm Optimization (PSO) method is employed to identify the unknown parameters of the generalised system, ALAM, based on the system measurement directly using a one-step scheme. Simulation studies are carried out for an IEEE 14-Bus power system and an IEEE 57-Bus power system. Simulation results show that the ALAM can represent the area load characters accurately under different operational conditions and at different power system states.

Measurement-based approach for identification of static load model of electric power system

The validity of electric power system simulation or prediction models depends on static load model. Measurement- based approach is the unique method to identify them adequately. The measured power depends on both load reaction to supply voltage alteration and random process of load alteration Basically, there is no any universal method that can single out the inherent static load model from experimental data. The paper offers a proprietary technique which is the particular solution of the task. The technique considers the selection of neighboring measurement pairs with the supply voltage altering significantly be-tween them, the exclusion of selected pairs by load power factor and subsequent selection of the inherent static load model presented as the polynomial load model. The usage of the technique to identify static load model at “Fenster” industrial enterprise （in Borisov city） is presented. The ideas considered in the paper can be used for future development of static load model identification methods with the data obtained during both active experiment and in other operating models of electric power systems.

Impact of Weather Conditions and Dynamic Load Models on Steady State and Dynamic Response of Power System

This paper gives an insight on the effect of transmission line temperature variations, resulting from loading and weather conditions changes, on a power system＇s steady state and dynamic performance. The impact of dynamic load models on system stability is also studied. The steady-state and dynamic stability simulation results of a 39 bus system for constant line impedance （the traditional simulation practice） are compared to the results with estimated, but realistic, temperature varied line impedances using PSLF （positive sequence load flow） software. The modulated line impedances will affect the thermal loading levels and voltage profiles of buses under steady state response, while the dynamic results will show improved damping in electro-mechanical oscillations at generator buses.

Load Forecasting of the Power System: An Investigation Based on the Method of Random Forest Regression

Accurate power load forecasting plays an important role in the power dispatching and security of grid.In this paper,a mathematical model for power load forecasting based on the random forest regression(RFR)was established.The input parameters of RFR model were determined by means of the grid search algorithm.The prediction results for this model were compared with those for several other common machine learning methods.It was found that the coefficient of determination(R^(2))of test set based on the RFR model was the highest,reaching 0.514 while the corresponding mean absolute error(MAE)and the mean squared error(MSE)were the lowest.Besides,the impacts of the air conditioning system used in summer on the power load were discussed.The calculation results showed that the introduction of indexes in the field of Heating,Ventilation and Air Conditioning(HVAC)could improve the prediction accuracy of test set.

Machine Learning for Hybrid Line Stability Ranking Index in Polynomial Load Modeling under Contingency Conditions

In the conventional technique,in the evaluation of the severity index,clustering and loading suffer from more iteration leading to more com-putational delay.Hence this research article identifies,a novel progression for fast predicting the severity of the line and clustering by incorporating machine learning aspects.The polynomial load modelling or ZIP(constant impedances(Z),Constant Current(I)and Constant active power(P))is developed in the IEEE-14 and Indian 118 bus systems considered for analysis of power system security.The process of finding the severity of the line using a Hybrid Line Stability Ranking Index(HLSRI)is used for assisting the concepts of machine learning with J48 algorithm,infers the superior affected lines by adopting the IEEE standards in concern to be compensated in maintaining the power system stability.The simulation is performed in the WEKA environment and deals with the supervisor learning in order based on severity to ensure the safety of power system.The Unified Power Flow Controller(UPFC),facts devices for the purpose of compensating the losses by maintaining the voltage characteristics.The finite element analysis findings are compared with the existing procedures and numerical equations for authentications.

Multi-Objective Optimal Dispatch Considering Wind Power and Interactive Load for Power System

With the rapid and large-scale development of renewable energy, the lack of new energy power transportation or consumption, and the shortage of grid peak-shifting ability have become increasingly serious. Aiming to the severe wind power curtailment issue, the characteristics of interactive load are studied upon the traditional day-ahead dispatch model to mitigate the influence of wind power fluctuation. A multi-objective optimal dispatch model with the minimum operating cost and power losses is built. Optimal power flow distribution is available when both generation and demand side participate in the resource allocation. The quantum particle swarm optimization (QPSO) algorithm is applied to convert multi-objective optimization problem into single objective optimization problem. The simulation results of IEEE 30-bus system verify that the proposed method can effectively reduce the operating cost and grid loss simultaneously enhancing the consumption of wind power.

Agricultural Load Modeling Based on Crop Evapotranspiration and Light Integration for Economic Operation of Greenhouse Power Systems

The threat of environmental degradation attracts great attention to clean energy production and transportation.However,the limited scope of energy consumption causes the large-scale of clean energy sources to be abandoned in Sichuan province.In the meantime,the development of modern greenhouse cultivation has transformed the agriculture industry to develop a brand-new type of electrical load in the grid.Consequently,the agricultural load can be used to consume the clean energy while facilitating plant growth.In this paper,an innovative agricultural load model is proposed based on crop evapotranspiration and daily light integration.Furthermore,the proposed agricultural load model is also applied to investigate the electricity consumption of five types of crop planting.The results illustrate that the power consumption is primarily driven by an artificial lighting compensation system.

In-Depth Study on Synthesis Load Modeling

Detailed procedures for the modeling of synthesis load models (SLM) of actual power network are presented, then the classification indices based on 1oad characteristics and the principle of load classification are expounded. Based on the results of general investigation of 220 kV and 330kV substations in China’s power grid, the load characteristics are classified, and detailed load characteristic investigation of selected different kinds of typical substations are carried out. By use of statistical synthesis, the SLM for typical substations, in which the distribution networks are taken into account, are built;and the modeling results are disseminated and applied to whole Chinese power grid. Besides, the load model parameters are evaluated and verified via post-disturbance simulation method. The effectiveness of the built SLM is validated by the fitting of some disturbance incidents.

Modeling load distribution for rural photovoltaic grid areas using image recognition

Expanding photovoltaic(PV)resources in rural-grid areas is an essential means to augment the share of solar energy in the energy landscape,aligning with the“carbon peaking and carbon neutrality”objectives.However,rural power grids often lack digitalization;thus,the load distribution within these areas is not fully known.This hinders the calculation of the available PV capacity and deduction of node voltages.This study proposes a load-distribution modeling approach based on remote-sensing image recognition in pursuit of a scientific framework for developing distributed PV resources in rural grid areas.First,houses in remote-sensing images are accurately recognized using deep-learning techniques based on the YOLOv5 model.The distribution of the houses is then used to estimate the load distribution in the grid area.Next,equally spaced and clustered distribution models are used to adaptively determine the location of the nodes and load power in the distribution lines.Finally,by calculating the connectivity matrix of the nodes,a minimum spanning tree is extracted,the topology of the network is constructed,and the node parameters of the load-distribution model are calculated.The proposed scheme is implemented in a software package and its efficacy is demonstrated by analyzing typical remote-sensing images of rural grid areas.The results underscore the ability of the proposed approach to effectively discern the distribution-line structure and compute the node parameters,thereby offering vital support for determining PV access capability.

A robust optimization model for demand response management with source-grid-load collaboration to consume wind-power

To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.

Estimation of composite load model with aggregate induction motor dynamic load for an isolated hybrid power system

It is well recognized that the voltage stability of a power system is affected by the load model and hence, to effectively analyze the reactive power compensation of an isolated hybrid wind-diesel based power system, the loads need to be considered along with the generators in a transient analysis. This paper gives a detailed mathematical modeling to compute the reactive power response with small voltage perturbation for composite load. The composite load is a combination of the static and dynamic load model. To develop this composite load model, the exponential load is used as a static load model and induction motors （IMs） are used as a dynamic load model. To analyze the dynamics of IM load, the fifth, third and first order model of IM are formulated and compared using differential equations solver in Matlab coding. Since the decentralized areas have many small consumers which may consist large numbers of IMs of small rating, it is not realistic to model either a single large rating unit or all small rating IMs together that are placed in the system. In place of using a single large rating IM, a group of motors are considered and then the aggregate model of IM is developed using the law of energy conservation. This aggregate model is used as a dynamic load model. For different simulation studies, especially in the area of voltage stability with reactive power compensation of an isolated hybrid power system, the transfer function AQ/AV of the composite load is required. The transfer function of the composite load is derived in this paper by successive derivation for the exponential model of static load and for the fifth and third order IM dynamic load model using state space model.

Dynamic equivalent method of interconnected power systems with consideration of motor loads

The existing equivalent methods usually only deal with static load models and neglect the dynamic characteristics of loads such as induction motors.This paper presents a dynamic equivalent method which considers motor dynamics.At first,the clustering criterion of motor loads is given.The motors with similar dynamic characteristics are classified into one group.Then,reduction of motors in the same group is carried out.Finally,parameters of the equivalent motor are calculated and the equivalent system is thus obtained.This aggregation method is applied to the New England system of 39-buses and 10-generators.Simulation results show that the equivalent system retains the dynamic performance of the original system with good accuracy.Compared with the 1-motor equivalent scheme,the 2-motor equivalent scheme can improve equivalent precision effectively.

Measurement-based load modeling:Theory and application

Load model is one of the most important elements in power system operation and control.However,owing to its complexity,load modeling is still an open and very difficult problem.Summarizing our work on measurement-based load modeling in China for more than twenty years,this paper systematically introduces the mathematical theory and applications regarding the load modeling.The flow chart and algorithms for measurement-based load modeling are presented.A composite load model structure with 13 parameters is also proposed.Analysis results based on the trajectory sensitivity theory indicate the importance of the load model pa-rameters for the identification.Case studies show the accuracy of the presented measurement-based load model.The load model thus built has been validated by field measurements all over China.Future working directions on measurement-based load modeling are also discussed in the paper.

Analytical Modeling of Disaster-induced Load Loss for Preventive Allocation of Mobile Power Sources in Urban Power Networks

Continuous power supply of urban power networks(UPNs)is quite essential for the public security of a city because the UPN acts as the basis for other infrastructure networks.In recent years,UPN is threatened by extreme weather events.An accurate modeling of load loss risk under extreme weather is quite essential for the preventive action of UPN.Con-sidering the forecast intensity of a typhoon disaster,this paper proposes analytical modeling of disaster-induced load loss for preventive allocation of mobile power sources(MPSs)in UPNs.First,based on the topological structure and fragility model of overhead lines and substations,we establish an analytical load loss model of multi-voltage-level UPN to quantify the spatial dis-tribution of disaster-induced load loss at the substation level.Second,according to the projected load loss distribution,a preventive allocation method of MPS is proposed,which makes the best use of MPS and dispatches the limited power supply to most vulnerable areas in the UPN.Finally,the proposed meth-od is validated by the case study of a practical UPN in China.

Day-Ahead Probabilistic Load Flow Analysis Considering Wind Power Forecast Error Correlation

Short-term power flow analysis has a significant influence on day-ahead generation schedule. This paper proposes a time series model and prediction error distribution model of wind power output. With the consideration of wind speed and wind power output forecast error’s correlation, the probabilistic distributions of transmission line flows during tomorrow’s 96 time intervals are obtained using cumulants combined Gram-Charlier expansion method. The probability density function and cumulative distribution function of transmission lines on each time interval could provide scheduling planners with more accurate and comprehensive information. Simulation in IEEE 39-bus system demonstrates effectiveness of the proposed model and algorithm.

Analysis of Commutation Failure in Multi-Infeed HVDC System under Different Load Models

HVDC technology has been widely used in modern power system. On one hand, HVDC has the advantages of economy, high efficiency and strong controllability. While on the other hand, it makes the dynamic characteristics of the power system becoming more and more complex. That puts forward a new challenge to system stability and raises new questions for power system simulation. This paper focuses on the interaction between AC and DC systems, especially the problem of commutation failure caused by AC system fault. Based on the data of China Southern Power Grid, this paper calculates the fault regions that may cause commutation failure and calculates the system critical clearance time under different load models, analyzes the impacts of different load models on commutation failure and the stability of AC/DC hybrid system.

A Weighted Combination Forecasting Model for Power Load Based on Forecasting Model Selection and Fuzzy Scale Joint Evaluation

To solve the medium and long term power load forecasting problem,the combination forecasting method is further expanded and a weighted combination forecasting model for power load is put forward.This model is divided into two stages which are forecasting model selection and weighted combination forecasting.Based on Markov chain conversion and cloud model,the forecasting model selection is implanted and several outstanding models are selected for the combination forecasting.For the weighted combination forecasting,a fuzzy scale joint evaluation method is proposed to determine the weight of selected forecasting model.The percentage error and mean absolute percentage error of weighted combination forecasting result of the power consumption in a certain area of China are 0.7439%and 0.3198%,respectively,while the maximum values of these two indexes of single forecasting models are 5.2278%and 1.9497%.It shows that the forecasting indexes of proposed model are improved significantly compared with the single forecasting models.