Mathematical Problems in Engineering

Volume 2017, Article ID 4030146, 12 pages

https://doi.org/10.1155/2017/4030146

## A MapReduce Based High Performance Neural Network in Enabling Fast Stability Assessment of Power Systems

^{1}School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, China^{2}School of Electronic and Computer Engineering, Brunel University London, Uxbridge UB8 3PH, UK

Correspondence should be addressed to Youbo Liu; nc.ude.ucs@obuoyuil

Received 2 May 2016; Revised 7 August 2016; Accepted 23 November 2016; Published 8 February 2017

Academic Editor: Huaguang Zhang

Copyright © 2017 Yang Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Transient stability assessment is playing a vital role in modern power systems. For this purpose, machine learning techniques have been widely employed to find critical conditions and recognize transient behaviors based on massive data analysis. However, an ever increasing volume of data generated from power systems poses a number of challenges to traditional machine learning techniques, which are computationally intensive running on standalone computers. This paper presents a MapReduce based high performance neural network to enable fast stability assessment of power systems. Hadoop, which is an open-source implementation of the MapReduce model, is first employed to parallelize the neural network. The parallel neural network is further enhanced with HaLoop to reduce the computation overhead incurred in the iteration process of the neural network. In addition, ensemble techniques are employed to accommodate the accuracy loss of the parallelized neural network in classification. The parallelized neural network is evaluated with both the IEEE 68-node system and a real power system from the aspects of computation speedup and stability assessment.

#### 1. Introduction

In recent decades, dozens of large power blackouts have occurred. Loss of stability has been widely recognized as the most critical factor that leads to power system collapse. Meanwhile, modern power systems are exposed to higher risks than ever before due to the increasingly stressed operation conditions caused by renewable energy penetrations, electricity market gaming, insufficient awareness technique, and shortage of investments [1]. These situations consequently reduce the dynamic stability of power systems when the severe disturbances occur.

Transient stability assessment (TSA) is an effective resort to evaluate dynamic security under various operations in control centers. To facilitate TSA, machine learning technologies have been widely applied in the past two decades, which is well summarized in an early literature [2]. Most of the existing works of the transient stability identification are focused on binary stable state prediction using clustering and classification. For example, Support Vector Machine, Decision Tree, and Artificial Neural Network (ANN) are the widely used approaches to detecting instability of power systems by using postfault trajectories within a few cycles [3–5]. On the other hand, a few of machine learning techniques have been investigated to enable dynamic coherency identification of power systems, providing critical information for system equivalents [6], islanding control [7], and area detection [8]. But coherency analysis has limited ability to determine the most disturbed units, which may lead to the eventual desynchronization.

Besides awareness of globally stable status, it is important for emergency control to understand which generator or group of generators have a tendency of desynchronization. Traditional stability predicators cannot point out the leading units while the coherency-based classification needs a longer time window to observe perturbance trajectories. The most feasible solution is to establish a set of trained predictors for each generator to enable individual identification [9]. But it is admitted that it is computational intensive due to the fact that a power system normally has hundreds of generators, which generate massive volumes of data. Few machine learning techniques have considered the impact of the critical unstable generators (CUGs) in TSA of power systems. As a result, it has become a challenge for standalone machine learning techniques running on single computers to deal with TSA taking into account the impact of massive CUGs [10]. For this purpose, the application of high performance computing techniques has become a necessity.

This paper presents HBPNN, a high performance back propagation neural network using MapReduce computing model. Hadoop [11–13], which is an open-source implementation of MapReduce, is first employed to parallelize the neural network. The parallelized neural network is further enhanced using HaLoop [14] to reduce the computation overhead incurred in the iteration process of the neural network. In addition, ensemble techniques are employed to maintain high accuracy in classification when datasets are split into small data chunks and processed in parallel nodes. The parallelized neural network is evaluated with both the IEEE 68-node system and a real power system from the aspects of computation speedup and stability assessment.

The rest of the paper is organized as follows. Section 2 discusses the related work about the application of machine learning techniques for TSA. Section 3 presents in detail the design of HBPNN. Section 4 evaluates the performance of the parallelized neural networks and analyzes the experimental results. Section 5 concludes the paper and points out the future work.

#### 2. Related Work

As wide area monitoring systems (WAMS) are now being deployed in large number of power systems, phasor measurement unit (PMU) is playing an ever increasingly vital role in dynamic security assessment [15]. A number of researches have been carried out to assess transient stability using PMU data. Among these research efforts, PMU trajectories based indicators are considered as efficient estimators to understand dynamic behaviors of power systems, especially in severe disturbances. For example, Alvarez and Mercado proposed seven trajectory based indices, which are suitable for fuzzy inference on real-time dynamic vulnerability [16]. Furthermore, Makarov et al. [17] presented a review on PMU-based security assessment offering a clear roadmap for further development.

Machine learning techniques have been widely employed for instability detection or stability margin estimation. However, few studies have been carried out for TSA by identifying CUGs in power systems due to massive volumes of data generated from the large number of the CUGs. For this purpose, this paper employs back propagation neural network (BPNN) to identify CUGs in a timely manner.

BPNN has proven to be effective in classification due to its gradient-descent feature that results in its remarkable function approximation. However, large-scale data processing brings a significant challenge to BPNN in computation. Rizwan et al. [18] employed a neural network on solar energy estimation. It is admitted that the large volume of data makes the data processing an extremely complex task, which affects the training efficiency severely. Wang et al. [19] pointed out that large-scale neural network becomes one of the mainstream tools for processing massive data. Al-Masri et al. [10] also applied adaptive neural network to evaluate stability for every single generator, aiming at providing more detailed stability information. But real power systems usually have hundreds of generators. It is admitted that standalone neural networks running on single computers can hardly handle the problem in a reasonable time.

In order to speed up the efficiency of BPNN, distributed computing technologies have been employed [20–22]. Gu et al. [23] presented a parallel neural network using in-memory data processing techniques to accelerate neural network. However, in their work the training data is simply segmented into data chunks without considering accuracy loss. Liu et al. [24] presented a MapReduce based parallel BPNN in processing a large set of mobile data. This work further employs AdaBoosting algorithm to accommodate the loss of accuracy of the parallelized neural work. Although AdaBoosting is a popular sampling technique, it may enlarge the weights of wrongly classified instances, which would deteriorate the algorithm accuracy. Another major limitation of this research lies in that it does not consider the high overhead of Hadoop in dealing with input and output files in the iteration process.

To solve the issue of processing large-scale data using BPNN in power system for stability analysis especially for identification of CUGs, the presented work in this paper employs HaLoop to reduce the high overhead incurred in computation iterations. It also proves feasibility of MapReduce based high performance neural network on efficient stability assessment, providing a general tool to parallelize the machine learning algorithms to facilitate coordinated training to a large number of generators.

#### 3. The Design of HBPNN

##### 3.1. BPNN

BPNN has been proved to be effective in classification. It employs feed-forward and back propagation mechanisms to train the parameters of the network.

In the feed-forward phase, let(i) denote weight from th neuron to th neuron,(ii) denote bias for varying the activity of the th neuron,(iii) denote output of the th neuron from last layer,(iv) denote output of the th neuron of the current layer,(v) denote input of the th neuron in hidden and output layers.

Therefore, can be represented by

In the neuron, the nonlinear equation is sigmoid function; therefore the output of the th neuron from the current layer to next layer can be represented by

The output layer finally outputs its . The feed-forward phase is completed.

In the back propagation phase, let(i) denote the error-sensitivity of certain layer,(ii) denote the desirable output of neuron in the output layer,(iii) denote error-sensitivity of one neuron in the last layer,(iv) represent corresponding weight of .

Therefore, in the output layer and in the hidden layers can be represented by

The weight and bias can be tuned, where denotes the learning speed:

The back propagation phase is completed. Afterward, a second round of training starts. BPNN terminates if (5) or (6) is satisfied or a certain number of iterations has been reached.

For executing a classification task, a trained BPNN only needs to execute the feed-forward phase. The classification result can be achieved from the output layer of the network.

##### 3.2. Time-Domain Simulation

The time-domain simulation of power system is modeled by means of differential algebraic equations (DAEs); the details of the model can be found in [25]. The outputs of the simulation, which are the status trajectories, can be utilized as the simulated PMU data for further analysis. In this study, an open-source package PST [26] is employed to simulate dynamic trajectories of concerned parameters for random faults in a certain interval of cycles.

##### 3.3. BPNN Based Transient Stability Assessment

If a power angle difference between any two generators and exceeds a specified threshold, for example, 270 or 360 degrees, the status of the system is considered as unstable. Alternatively, the criterion using the center of inertia (COI) is usually applied to identify power system stability, which is expressed aswhere and represent rotor angle and inertia constant of generator , is the sum of , is the number of generators, and is instability threshold which is defined as 180 degrees in this paper.

The training phase of BPNN based TSA is illustrated in Figure 1.