31 results about "Dynamic security assessment" patented technology

The invention discloses an online evaluation method of electric power system dynamic stability. The online evaluation method comprises the steps of step 1, carrying out high-frequency sampling by utilizing a PMU (Power Management Unit), and extracting an oscillation mode for sampling data by utilizing a matrix pencil identification method; step 2, detecting low-frequency oscillation, and emitting low-frequency oscillation warning if a damping ratio of the detected oscillation mode is lower than a set threshold value; step 3, carrying out small perturbation calculation on a system model in a current running mode through a frequency domain analysis method, and obtaining a characteristic value, a characteristic vector, a corresponding unit and participated factor information; and step 4, screening out information of a low-frequency oscillation mode from a small perturbation calculation result according to the low-frequency oscillation mode which is determined by the matrix pencil identification method, regulating a related electric power generator to output, and obtaining tide stability limit of call lines between related domains through time-domain simulation at the same time. According to the online evaluation method disclosed by the invention, the influence of noise is effectively avoided, the identification accuracy is improved, the calculating speed and the calculating efficiency are increased, and the online dynamic safety evaluation effect of the electric power system is obviously improved.

The invention provides a power system dynamic security assessment comprehensive model and a spatiotemporal visualization method. The method comprises the following steps: constructing a dynamic security index and establish an initial sample set for dynamic security assessment based on the historical operation data of a power system and a predicted accident set; 2) constructing a feature selectionframework, performing feature selection on the initial sample set and forming a processed high-efficiency sample set; 3) constructing an online dynamic security assessment comprehensive model based ona random bit forest and performing offline training and updating on the model by using the high-efficiency sample set; and 4) using the continuously updating dynamic security assessment model to complete the online assessment of the dynamic security state and using the spatiotemporal visualization method to realize the visual presentation of the dynamic security information. The purpose of the present invention is to provide the online dynamic security assessment comprehensive model and the spatiotemporal visualization method which are beneficial to the system operators to take timely preventive and control measures, avoid large power outage caused by accidents and improve the secure operation level of the power grid.

Disclosed is a power distribution network fault recovery method based on real-time contingency sets. The method includes the steps of determining a contingency severity index firstly, obtaining the expected value of influence of contingencies by combining the contingency severity index with the probability of occurrence of the contingencies, sorting the contingencies according to the severity levels according to the expected value of influence of the contingencies, screening out the contingency sets needing fault recovery analysis, updating the contingency sets within each time period, and outputting corresponding fault recovery schemes in real time. The real-time contingency sets are established, the contingencies with the small expected value of influence are omitted, only the fault recovery schemes of the contingencies with the high expected value of influence are analyzed, the number of the contingencies needing to be analyzed by on-line dynamic safety assessment is largely decreased, the calculation time for analysis of the fault recovery schemes is shortened, and real-time fault recovery plans are achieved before faults occur.

The invention provides a trend load flow calculating method based on online status estimation and real-time scheduling plans. The method comprises the following steps of determining future trend operating mode data of a power grid; performing rationality identification and automatic adjustment on real-time scheduling plans; establishing a multi-section active power control model to perform automatic fine-tuning generation active power control; performing reactive power voltage local control, and distributing reactive power unbalance of hub nodes; performing load flow calculation to generate load flow calculation data used for power grid future trend dynamic safety assessment. The trend load flow calculating method based on online status estimation and real-time scheduling plans solves the problem that traditional online dynamic safety assessment algorithms cannot effectively analysis future power grid short safety and stability within a short time, and by analyzing further power grid safety change trend in advance, assists scheduling personnel to arrange and adjust current operating manners.

The invention provides an online dynamic security assessment method based on mutual information and iterated random forest, which comprises the steps of creating a data set containing a large number of operation variables and transient stability margins (TSMs) based on historical operation data of the power system and simulation of a series of faults; carrying out feature selection on the data setby using partial mutual information (PMI) to screen out key variables; sending the key variables to an iterated random forest (IRF) regressor for training; and immediately providing a real-time assessment result by a trained dynamic security assessment (DSA) model by receiving the selected variables in real time from a wide area measurement system (WAMS) server. The online DSA model of the powersystem provided by the invention can provide rapid and efficient assessment for the power grid, thereby facilitating the system maintenance and security measure prevention work of the power personnel,and having important significance for the operation security and quality of the power grid.

The invention discloses a generative adversarial network-based power system dynamic security assessment method. The method comprises the following steps of 1, constructing an initial sample set containing a large number of operation variables and transient stability margin security indexes; 2, constructing an efficient sample set containing key feature data and TSMs labels; 3, obtaining a GAN model and a dynamic security evaluation model capable of accurately filling missing data; 4, detecting the integrity of PMUs data in real time through the intelligent system, and if the data is complete,directly inputting the data into the DSA model for DSA of the power system; and if there is data missing, inputting incomplete data collected by the PMUs into the GAN model to fill the missing data, and inputting the filled complete data set into the DSA model to perform DSA of the power system. According to the electric power system DSA method based on the GAN, missing data can be rapidly and accurately filled and DSA can be performed on the system for the problem of data missing of the electric power system so that electric power operation personnel are enabled to rapidly take control measures and occurrence of faults and economic loss can be avoided.

This invention relates to a method of determining stability of unstable equilibrium point (UEP) computed by using BCU method, comprising selecting UEP computed by using BCU method, obtaining a test vector Xtest for the selected UEP, say XUEP using the following equation: <?in-line-formulae description="In-line Formulae" end="lead"?>Xtest=Xspost+0.99(XUEP-Xspost) <?in-line-formulae description="In-line Formulae" end="tail"?> where Xspost is the SEP, and checking boundary condition of XUEP by simulating system trajectory of post-fault original system starting from Xtest.

The invention provides a memory-based online data integration method. Effects of exactly and quickly judging the state of a power supply system under a specific fault, meeting the stability analysis of an intelligent power grid scheduling technique supporting system and online dynamic safety estimation on the power supply system, and obtaining practical data capable of being applied to load flow calculation of a large power supply system can be realized by quickly and effectively reading online / offline data and reading in a server in a certain form. According to the memory-based online data integration method disclosed by the invention, a problem that a complex power supply system can not model and analyze when a running state of the complex power supply system has large change such as catastrophe at a trend of large power grid interconnection is solved.

A power system dynamic safety confidence evaluation method based on an integrated learning scheme comprises the steps that firstly, feature automatic sorting is conducted on input feature full-set data, so that feature selection of the next stage is facilitated, and a high-quality training set is provided for sample training; independent extreme learning machine (ELM) training is carried out on the sample set, and finally, model fitting is carried out on the trained ELM to achieve integrated learning, thereby obtaining reliable information. The method is beneficial to the unique property of ELM; training and decision rules are strategically designed; an intelligent system (ISS) can learn and work quickly, potential risks can be recognized, and the method and the device have very importantsignificance in providing effective evaluation on the credibility of dynamic security assessment (Dynamic Assertion, DSA) results.

The invention discloses an online dynamic safety assessment scheme based on random forest and extreme learning regression. The method specifically comprises the following steps: (1) obtaining an electric power system operation data sample by utilizing historical operation data of an electric power system and fault simulation based on an anticipated accident set, constructing a dynamic safety indexand forming an original sample set; (2) obtaining a key variable by utilizing a Gini index and a variable importance score by adopting a feature selection method based on a random forest; (3) training an extreme learning regression machine by using the key variables to obtain a mapping relationship; (4) updating the model by receiving the real-time operation data of the power system from the wide-area measurement system server, thereby completing real-time dynamic safety evaluation of the power system. According to the scheme, rapid and efficient real-time safety assessment is carried out onthe power system, and stable operation of the power system can be maintained.

A data driving method for dynamic safety assessment of an electric power system comprises the following steps: 1), obtaining operation data of the electric power system, constructing corresponding dynamic safety indexes, and constructing an initial sample set containing an operation data set and the dynamic safety indexes; 2) processing the initial sample set to achieve the purposes of data compression and dimension reduction, and generating an efficient sample set; 3) obtaining a dynamic safety assessment model; 4), obtaining a near-real-time updating sample set, and completing the updating of the dynamic safety evaluation model; and 5) completing evaluation of the real-time dynamic safety state of the power system to obtain a real-time dynamic safety evaluation result. The invention aimsto provide the dynamic safety assessment method which is beneficial to improving the assessment speed and the prediction precision, is beneficial to system operation personnel to take prevention andcontrol measures in time, and improves the operation stability and the power supply reliability of a power system.

This invention relates to a method of determining stability of unstable equilibrium point (UEP) computed by using BCU method, comprising selecting UEP computed by using BCU method, obtaining a test vector Xtest for the selected UEP, say XUEP using the following equation:Xtest=Xspost+0.99(XUEP−Xspost)where Xspost is the SEP, and checking boundary condition of XUEP by simulating system trajectory of post-fault original system starting from Xtest.

A dynamic security misclassification constraint method for a power system based on an umbrella algorithm comprises the following steps: 1, constructing a corresponding dynamic security classificationlabel based on a dynamic security classification rule; 2, performing feature selection on an initial sample set constructed based on a historical operation database of the power system to obtain a keyfeature set; 3, constructing an online dynamic security evaluation model based on a Nelmann Pearson comprehensive classifier of an umbrella algorithm, and sending the key feature set and the corresponding dynamic security classification label as input into the classifier for offline training; 4, considering various power system operation influence factors, performing time domain simulation to obtain an updated sample set, and sending the updated sample set into a comprehensive NP classifier for model updating; and 5, carrying out online DSA through synchronous phasor measurement unit data collected in real time. The dynamic security misclassification constraint method can reduce the impact on the safe operation of a power system from DSA wrong classification, and reduces the social and economic loss.

A power system online dynamic safety assessment method based on an integrated decision tree comprises the following steps: 1) sorting and screening predicted accidents, and establishing an initial knowledge base required by offline training by using a screened dominant accident set; 2) based on the initial knowledge base, constructing a lifting type integrated decision tree and carrying out offline training on the decision tree; Step 3): reasonably creating a new training sample, combining the new training sample with the initial knowledge base, and updating the decision tree by using the newknowledge base; And 4) carrying out online dynamic safety assessment on the power system by utilizing the updated decision tree and a distributed processing technology. The invention aims to provide apower system safety assessment method which can avoid a power failure accident and improve the safety operation level of a power grid.

The invention discloses a power grid operation safety evaluation method based on a particle swarm algorithm and a gradient boosting tree. The method comprises the following steps: collecting the operation data of a power system, carrying out the simulation based on an anticipated accident set, obtaining a fault sample, and forming a sample set; performing feature selection on the sample set to obtain a key variable capable of being used for predicting the state of the power system; constructing an online dynamic safety evaluation model by combining a gradient boosting tree and ensemble learning, carrying out offline training and updating on the model by utilizing key variables, and carrying out regression prediction on the state of the power system; and inputting the real-time power systemoperation data into the constructed online dynamic safety evaluation model, and carrying out real-time dynamic safety evaluation on the power grid. The power system online dynamic safety evaluation model provided by the invention can provide quick and efficient evaluation for the power grid, is beneficial to system maintenance and safety measure prevention work of power personnel, and has great significance for improving the safety and quality of power grid operation.

The invention discloses an online dynamic safety assessment method for a power system based on an integrated classification model. The method comprises the steps: 1, establishing an initial sample setfor safety assessment of the power system; 2, constructing an efficient sample set containing key variables and corresponding dynamic security classification labels through feature selection; 3, obtaining a dynamic safety evaluation model capable of accurately predicting the operation state of the power system; and 4, taking the real-time data as the input of the dynamic safety assessment model to realize the online dynamic safety assessment of the power system. The objective of the invention is to solve the problem of imbalance of evaluation data and evaluation errors caused by misclassification in the DSA process of a power system.

The invention provides a power grid dynamic safety assessment method based on blind area identification and electrical coordinate system expansion. The method comprises the following steps: acquiring a fault position node and a steady-state power flow characteristic of a power grid; obtaining an electrical coordinate of the fault position node in the extended electrical coordinate system; according to the obtained electrical coordinates and the steady-state power flow characteristics, combining a pre-trained dynamic safety evaluation model to obtain a dynamic safety evaluation result of the power grid, wherein an initial electrical coordinate system is constructed according to acquired power grid network parameters, blind line search is performed according to the initial electrical coordinate system to obtain an initial blind area, the blind area is finely adjusted, reference nodes are expanded for the finely adjusted blind area, and an expanded electrical coordinate system is obtained by combining the expanded reference nodes. According to the invention, the fault position characterization precision of the blind area is improved, and the accuracy of dynamic safety evaluation of the power grid is further improved.

The invention discloses a dynamic safety assessment method based on an extreme gradient boosting decision tree. The method comprises the steps: 1, establishing an original sample set; 2, forming an intermediate sample set; 3, forming an efficient sample set; 4, performing offline training on the extreme gradient boosting decision tree by utilizing the efficient sample set, and constructing a dynamic safety evaluation model based on the XGBoost decision tree; 5, due to the influence of various operation factors of the power system, updating the efficient sample set through time domain simulation, and updating aDSA model by using the updated sample set; 6, based on the real-time measurement data of the synchronous phasor measurement unit, performing online DSA on the power system by using the DSA model. The invention aims to provide a dynamic safety assessment method based on an extreme gradient boosting decision tree. According to the method, online DSA can be efficiently and accuratelycarried out on the power system, the robustness of the evaluation model is good, and the method can adapt to the change of a network topology structure.

The invention provides an electric power system dynamic safety assessment system based on a decision tree (DT) and a preventive control method, and the method comprises the following steps of providing a safety boundary, and carrying out the separation and recognition of the safety boundary; deflecting the sample to a security boundary by using importance sampling; forming a database through timedomain simulation and DT training; and performing online dynamic safety assessment and executing prevention control. According to the invention, more uncertainties in the operation of the power transmission system are solved; the power system can operate more efficiently and safely, a traditional single decision tree assessment method is improved, two decision tree cooperation methods are adopted,the safe operation conditions and the prevention and control schemes of the safe operation conditions are accurately decided, and the method has important significance for improving the operation efficiency and quality of a power grid.

This invention relates to a method of determining stability of unstable equilibrium point (UEP) computed by using BCU method, comprising selecting UEP computed by using BCU method, obtaining a test vector Xtest for the selected UEP, say XUEP using the following equation:Xtest=Xspost+0.99(XUEP−Xspost)where Xspost is the SEP, and checking boundary condition of XUEP by simulating system trajectory of post-fault original system starting from Xtest.

The invention discloses an electric power system safety evaluation method based on correlation and redundancy detection. The method comprises the steps that 1, an original database containing a largenumber of power system operation variables and dynamic security classification tags is constructed based on historical operation data of a power system and dynamic simulation under a large number of anticipated accidents; 2, performing feature selection on the original database, and selecting a variable with high relevancy with the class label from a large number of operation variables as a key feature to form an efficient database; 3, constructing a dynamic safety evaluation model of the power system by combining a genetic algorithm and a BP neural network, and carrying out offline training and updating on the evaluation model by utilizing an efficient database; and step 4, based on real-time measurement data of a synchronous phasor measurement unit (PMU), carrying out online dynamic safety evaluation on the power system by utilizing the evaluation model. The method can quickly achieve the discrimination of the stability of the power system, guarantees the higher accuracy and reliability, and meets the online evaluation requirements of the power system.

An electric power system dynamic safety assessment method based on a hybrid integration model comprises the following steps: 1, constructing corresponding dynamic safety indexes, and establishing an initial sample set containing a plurality of electric power system operation variables and the corresponding safety indexes; 2, constructing an efficient sample set; 3, obtaining a dynamic safety evaluation model capable of accurately evaluating the operation condition of the power system; and step 4, based on real-time monitoring data of a synchronous vector measurement device, selecting key characteristic variable data and inputting the key characteristic variable data into a DSA model to realize online DSA of the power system. The invention is to provide an electric power system dynamic safety assessment method which is good in generalization ability, and the method has the advantages of being rapid and accurate in assessment, is beneficial for power grid workers to take effective prevention and control measures in time, and can effectively avoid loss caused by faults of the power system.

The invention relates to a dynamic security assessment method based on principal component analysis and a convolutional neural network, which specifically comprises the following steps of: 1, obtaining a system operation data sample, constructing a dynamic security index, and forming a corresponding initial sample set; 2, generating an efficient sample set; 3, updating the efficient sample set, and completing the updating of an evaluation model; and 4, based on the real-time operation data of a power system, completing evaluation of the real-time dynamic security state of the power system by using the continuously updated dynamic security evaluation model to obtain a final online dynamic security evaluation result. According to the online dynamic security model of the power system, rapid,efficient and accurate prediction and evaluation can be provided for the power system, system maintenance and safety measure prevention work of power personnel are facilitated, the safety and stability of operation of the power system are improved, and the reliability of power supply is improved.

A risk index-based dynamic safety assessment method for power systems, including establishing a risk model based on the probability of the expected accident and the impact of the expected accident, screening and sorting the expected accidents, and obtaining the dominant expected accident set; Carry out load flow analysis calculation and record the voltage of all nodes in the system for the leading anticipated accident V and voltage phase angle δ ; Based on the polynomial form of the transient stability margin and the obtained power flow data, calculate the probability distribution of the transient stability margin and its mean and standard deviation; based on the established risk model, use the probability distribution of the transient stability margin and The severity function calculates the risk index of the expected accident; based on the risk index sought, the corresponding preventive control and emergency control measures are taken. The method of the present invention considers the uncertainty of load prediction, quantitatively combines the probability and severity of accidents, which are two factors that determine system safety, and can comprehensively reflect the impact of accidents on the entire power system, thereby better coordinating The relationship between the safety and economy of power system operation.

The invention discloses an actual measurement based machine network dynamic safety assessment system and the method thereof, which belong to the field of measurement and dynamic stable control techniques of power systems. The technical proposal is as follows: the system comprises the following six parts: a microcomputer machine network parametric comprehensive measurement device, a remote management systems, a background analytical software system, a machine network coupling modeling system, a power grid data information platform and a dynamic safety assessment system. The method comprises the following steps: arranging the microcomputer machine network parametric comprehensive measurement device in a power plant, collecting field data of the steam turbine, the power generator and the dynamic process of the power grid, transmitting the data to the data center via the network or the communication means, carrying out a series of analysis processing of the data to obtain the machine network dynamic coupling model and parameters, and completing the dynamic safety assessment and the time-domain simulation and verification of the power system. The system and the method establish the close relation between the dynamic safety and stability of the power system and the dynamic process stability of the power generator set and the power grid and provide the guarantee for the machine network coordinative operation of the power system.

Disclosed is a power distribution network fault recovery method based on real-time contingency sets. The method includes the steps of determining a contingency severity index firstly, obtaining the expected value of influence of contingencies by combining the contingency severity index with the probability of occurrence of the contingencies, sorting the contingencies according to the severity levels according to the expected value of influence of the contingencies, screening out the contingency sets needing fault recovery analysis, updating the contingency sets within each time period, and outputting corresponding fault recovery schemes in real time. The real-time contingency sets are established, the contingencies with the small expected value of influence are omitted, only the fault recovery schemes of the contingencies with the high expected value of influence are analyzed, the number of the contingencies needing to be analyzed by on-line dynamic safety assessment is largely decreased, the calculation time for analysis of the fault recovery schemes is shortened, and real-time fault recovery plans are achieved before faults occur.

The invention discloses a power system transient stability judgment system and method based on deep learning, which belongs to the technical field of power system dynamic security assessment. The system of the invention includes four modules: sample acquisition, feature extraction, sample expansion, and stability judgment. At the same time, the method of system implementation is disclosed. Using the deep self-encoder to extract the characteristic data of the power system sample data reduces the dimension of the sample, which can effectively save computing power and computing time; using the confrontation generation network to generate pseudo-sample data, expand the scale of the sample set, The stability judgment module can better judge the transient stability of the power system and improve the accuracy of stability judgment; the use of deep convolutional neural network to build the power system transient stability judgment module can make more real-time and accurate judgments Transient stability of power systems.