Patent Application
20220115871
The present invention relates to machine learning of grid power oscillations.
With the growth in size of interconnected power systems and the participation of unsynchronized distributed energy resources, the phenomenon of oscillation has become common and widespread. Insufficient damped oscillations reduce the system margin and increase the risk of instability and cascading failure. Thus, timely and precise control response is crucial.
Oscillations are typically classified as either natural or forced, based on their initial causes. Natural oscillation is caused by a lack of system damping and is triggered by disturbance. Forced oscillation is due to periodic energy injection into the system and can occur even when system damping is sufficient. The most common control strategy for natural oscillations is to adjust the power system stabilizer. The most effective control for forced oscillations is to locate the disturbance source. Thus, distinguishing the two types of oscillations is a prerequisite for the effective damping of oscillations.
Oscillation classifications have been attracting more attention in the past decade. Envelope based approaches have been proposed in which an increase in amplitude is used to distinguish natural oscillations from forced ones. However, the accuracy of the classification depends on the size of the envelope, since the algorithm is found failing when the oscillation is lightly damped. The performance of the spectral method is shown to degrade when the forced oscillation has a frequency close to a system mode frequency. A power spectral density and kurtosis based approach has been used, which is simple and accurate when there is a long time period of data. However, the long-time data requirement limits the method as an off-line application.
Thus, the state of the art in oscillation classification methods typically tends to extract some features of different mechanisms and then summarize them to a given index. This is followed by application of simple (linear) logic rules for the classification of oscillation events. This approach usually is complicated and considerable oscillation event information is lost in the process. Moreover, the rules are typically linear and over-simplified.
Machine learning techniques are used to identify oscillation mechanisms that can keep intact as much information as possible of the system while simultaneously addressing the common problem of lack of data in the system.
In one aspect, a framework is used to automatically extract features to distinguish natural and forced oscillations and keep as much information about the system as possible. Second, to overcome the impact of detection of starting points of oscillations, a time augmentation approach is used. Third, a transfer learning approach is applied to transfer models between different systems, which helps to resolve the problem of lack of training data.
In another aspect, a method to distinguish oscillations in a power grid includes:
In a further aspect, a power grid includes power generators; one or more power consumers; power grid to transmit power from generators to consumers; and a neural network coupled to the power grid to distinguish oscillations in the power grid. The neural network comprising code for:
Advantages of the system may include one or more of the following. The system helps generators and loads interconnected through a network to operated in synchronization at a constant system frequency. If the speed of one generator deviates from the synchronous speed, the power change affects all other generators in the system. When this happens, the system maintains synchronous speed by applying the appropriate control action, such as altering the controllers in the exciter or turbine. The system reduces occurrences of low-frequency oscillation and can also fix the high-speed excitation system (used to prevent the loss of synchronizing torque and to improve transient stability) and avoid the damping characteristics of low-frequency oscillation.
The following figures are for illustration purposes only and are not drawn to scale. The exemplary embodiments, both as to organization and method of operation, may best be understood by reference to the detailed description which follows taken in conjunction with the accompanying drawings in which:
Xang: the data matrix comprised by generator angles.
Xang,i[t]: the generator angle data point at time instant t of generator i.
1 Approaches
In the preferred embodiment, distinguishing between natural and forced oscillations is formulated as a supervised learning process. In a supervised learning, oscillation data is collected. Features are extracted, and oscillation types are labelled by domain experts. Features and labels are fed to supervised learning algorithms to train a classifier model. The trained classifier can be used online to distinguish oscillation mechanisms. The key points during this process are feature extraction and classifier model selection. Feature extraction is the most important part. Correct extraction needs to reserve all information to train classifier models and remove other noise information. Another requirement of feature extraction is to reduce the volume of data, i.e., the size of feature should be as small as possible. We proposed to use a CNN model to automatically extract the feature. The process of the approach is shown in
1.1 Convolutional Neural Networks
The convolutional neural network (CNN) is shown in
As shown in
After a convolution layers, a pooling layer is constructed to reduce the dimension. Typical pooling includes maximum pooling and mean pooling. As shown in
After several convolution and pooling layers, the result is passed to a fully connected layer and a classification layer which are like the ones in other neural networks.
1.2 Feature Selection
Preferably, we select the nonlinear phase of oscillations as the input, i.e., the beginning period of oscillations. Considering it is hard to detect precisely the beginning point of oscillations, a sliding window with a 5 second width is applied to samples. In this way, multiple clips of samples with different beginning points is generated using one piece of data. Furthermore, each clip of sample is normalized to its z-score, where z[t]=(x[t]−μ(x))/σ(x), μ(x) and σ(x) are the mean and standard deviation of time series X, to eliminate the impact of absolute values.
For CNN model, the feature extraction process is mainly dealt by the convolution process, which makes the procedure easier. Three time variant matrix are constructed using generator angle, voltage, and speed.
In Equation (1-1), a matrix of generator angle is constructed, where N is the number generators and T is the number of time instances. The same process is carried out for generator voltage and speed. The construction process can be found in
1.3 Data Augmentation
In real-time application, the detection of the beginning of oscillations are not accurate. A data augmentation method is used to overcome this problem. For each clip of training data, ten samples are generated by sliding a window with width of 5 second and step size of 0.2 second, i.e. the 10th sample is 1.8 seconds later than the first one. To generate a clip of test data, a starting point uniformly distributed among [0,2] is first generate. Then a clip of data with the randomly generated starting point and window width 5 second is sampled from the simulation data. The process of data augmentation can be found in FIG.
1.4 Transfer Learning
Transfer of learning techniques across different test systems and real-world data to validate the performance is done next. Learning transfer takes a pre-trained neural network and use samples from other systems or scenarios to retrain (part of) the network and complete other tasks.
In
2 Case Study
To generate some training data, the Kundur 2-Area-4-Machine (2A4M) and WECC 179-Bus (179Bus) test systems are simulated using Transient Security Assessment Tool (TSAT). To clarify, the samples does not need to be generated in these two systems, in this way, or even using synthetic model. Here, we just want to give an example. For nature oscillation cases, the damping factor of each generator is set to a random value uniformly distributed among [0,4]. Further, loads at each bus are multiplied by factors uniformly distributed among [0.9,1.1] to mimic the randomness in operation conditions. A three-phase fault is added to a random bus and cleared 0.5 second after to trigger oscillations. Other parameters are kept unchanged.
For forced oscillation cases, a sinusoid with a frequency of 0.86 Hz is added to the exciter of a randomly picked generator, and the damping factor of the chosen generator is set to 0 to mimic the injected oscillation source. Loads at each bus are multiplied by factors uniformly distributed among [0.9,1.1]. Other parameters are kept unchanged.
Four hundred nature oscillations and four hundred forced oscillations are generated for 2A4M system, and nine hundred nature oscillations and fourteen hundred forced oscillations are generated for 179Bus systems. After the generation of raw data, a Gaussian distributed factor is multiplied to each measurement to simulate the measurement noise.
2.1 Classification Results without transfer learning
Monte Carlo simulations are carried out to validate the performance of different approaches. In each Monte Carlo run, the labeled data is separated to training set and testing set randomly with a ratio 0.8/0.2.
Various models are trained using the training set and test on the test set. A kurtosis-based method is adopted as a benchmark, which adopt a threshold of kurtosis of data to distinguish oscillation classes. The threshold of kurtosis is set to −0.5. The accuracy is averaged over all Monte Carlo simulations and shown in Table 2-1. All machine learning models perform well, which indicates the efficiency of the features in identification of the oscillation types. However, the kurtosis method performs not desired due to the short period of data and the failure to capture the beginning point of oscillations.
2.2 Classification Results with Transfer Learning
In this subsection, the CNN model is first trained using all labeled data from one system, retrained using 1% data, and tested using the rest data from the second system. Since the input dimension is different for two simulation systems. Thus, the input layers need to be replaced and retrained, and the retrained CNN model can not be applied directly back to the original training system. During the retraining process, the learning rate of the inherited network is set to 0.001 and the maximum number of epochs is set to 5 so that the inherited network is frozen. The learning rate of other parts are set 20 times larger.
The result of the CNN models is summarized in Table 2-2. The high accuracy demonstrates the outstanding performance of retrained CNN models.
An example of test bed can be found in FIG. A. The test bed models the exemplary Power Grid and Sensor Network of
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims. As used herein, the term “module” or “component” may refer to software objects or routines that execute on the computing system. The different components, modules, engines, and services described herein may be implemented as objects or processes that execute on the computing system (e.g., as separate threads). While the system and methods described herein may be preferably implemented in software, implementations in hardware or a combination of software and hardware are also possible and contemplated. In this description, a “computing entity” may be any computing system as previously defined herein, or any module or combination of modulates running on a computing system. All examples and conditional language recited herein are intended for pedagogical objects to aid the reader in understanding the invention and the concepts contributed by the inventor to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Although embodiments of the present inventions have been described in detail, it should be understood that the various changes, substitutions, and alterations could be made hereto without departing from the spirit and scope of the invention.
