BPSK 865/915 MHz PHY

As for the frequency band of 2450 MHz, the name of these specifications is BPSK PHY since the signal is BPSK modulated. For the chip modulation, a direct sequence spread spectrum is used. Each input bit is mapped into 15-chip pseudo-noise (PN) sequence as described in Table 3.7 [27]. The data symbol are then assigned using a differential encoding.

3.6 Performance Analysis of ZigBee System in the Presence of Impulsive Noise 65

Table 3.6: Symbol-to-chip mapping for the 2.4 GHz band.

Data symbols (dec) Chip values (c₀, c₁, . . . , c₃₁)

0 1 1 0 1 1 0 0 1 1 1 0 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 1 0 1 1 1 0 1 1 1 1 0 1 1 0 1 1 0 0 1 1 1 0 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 1 0 2 0 0 1 0 1 1 1 0 1 1 0 1 1 0 0 1 1 1 0 0 0 0 1 1 0 1 0 1 0 0 1 0 3 0 0 1 0 0 0 1 0 1 1 1 0 1 1 0 1 1 0 0 1 1 1 0 0 0 0 1 1 0 1 0 1 4 0 1 0 1 0 0 1 0 0 0 1 0 1 1 1 0 1 1 0 1 1 0 0 1 1 1 0 0 0 0 1 1 5 0 0 1 1 0 1 0 1 0 0 1 0 0 0 1 0 1 1 1 0 1 1 0 1 1 0 0 1 1 1 0 0 6 1 1 0 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 1 0 1 1 1 0 1 1 0 1 1 0 0 1 7 1 0 0 1 1 1 0 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 1 0 1 1 1 0 1 1 0 1 8 1 0 0 0 1 1 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 1 1 1 0 1 1 1 1 0 1 1 9 1 0 1 1 1 0 0 0 1 1 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 1 1 1 0 1 1 1 10 0 1 1 1 1 0 1 1 1 0 0 0 1 1 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 1 1 1 11 0 1 1 1 0 1 1 1 1 0 1 1 1 0 0 0 1 1 0 0 1 0 0 1 0 1 1 0 0 0 0 0 12 0 0 0 0 0 1 1 1 0 1 1 1 1 0 1 1 1 0 0 0 1 1 0 0 1 0 0 1 0 1 1 0 13 0 1 1 0 0 0 0 0 0 1 1 1 0 1 1 1 1 0 1 1 1 0 0 0 1 1 0 0 1 0 0 1 14 1 0 0 1 0 1 1 0 0 0 0 0 0 1 1 1 0 1 1 1 1 0 1 1 1 0 0 0 1 1 0 0 15 1 1 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 1 1 1 0 1 1 1 1 0 1 1 1 0 0 0

Table 3.7: Symbol-to-chip mapping for the 865/915 MHz band.

Input bits Chip values (c₀, c₁, . . . , c₁₄)

1 1 1 1 1 0 1 0 1 1 0 0 1 0 0 0 2 0 0 0 0 1 0 1 0 0 1 1 0 1 1 1

Figure 3.23: Diagram of BPSK PHY simulation.

It performs an or exclusive or addition modulo 2 of a raw data bit with its prior encoded bit. A raised-cosine pulse shape filter with a roll-off factor equal to 1 is used. It is represented by equation 3.18.

f (t) = sin(πt/Tc) πt/Tc

cos(πt/Tc)

Figure 3.24: Performance of ZigBee in AWGN channel impaired by impulsive noise.

3.6.2.2 Performance Analysis

Considering the simulation diagrams depicted in Figs. 3.22 and 3.23, we evaluate the BER as a function of EbNo for the three bands in AWGN and Rayleigh’s channel impaired by Au impulsive noise. The results are depicted in Figs. 3.24 and 3.24, respectively. As can be seen in these figures, the performance are improved compared to the narrowband system. We note a gain of approximately 14 dB in AWGN channel and 8 dB for Rayleigh channel. For the BPSK PHY, the degradation is also noticeable in both AWGN and Rayleigh channel. Despite these gains, the performance are not globally satisfactory since they do not allow us to meet the requirements in HV substations.

3.7

Conclusion

In this chapter, different technologies for wireless sensor networks have been presented. The substation environment and impulsive noise sources are also studied. The methods for measuring and representing the partial discharges occurring in the substations have also been introduced. We have also examined the impact of impulsive noise on a ZigBee narrowband receiver for AWGN, Rayleigh, and Rician channels. The results showed that the impulsive noise influence is close to a Gaussian noise or a Rayleigh noise according to the SNR. After that, the spreading as defined by the protocol is implemented. We

3.7 Conclusion 67

Figure 3.25: Performance of ZigBee in Rayleigh channel impaired by impulsive noise.

evaluate the performance of the three bands, i.e., the OQPSK PHY and BPSK PHY in AWGN and Rayleigh’s channels impaired by Au impulsive noise. The results show that the performance are improved compared to the narrowband system. However, the obtained results are not satisfactory and do not allow us to meet the requirements for the deployment of SG applications in HV substations. Then, when considering a fading environment as the substation, the entire multi-path cannot be exploited by ZigBee due to the constraints of the distance (200 m). Indeed, several SG applications require a high data rate and a broad bandwidth. Standard wireless communication protocols as ZigBee cannot answer those needs. For this purpose, a reliable and efficient physical layer needs to be proposed leading to significant improvements. Next chapter will discuss solutions for mitigating the impulsive noise in power grid environment.

Chapter

4

Impulsive noise mitigation with coded-OFDM

system

Contents

4.1 Introduction . . . . 70 4.2 Impulsive Noise Reduction Approaches . . . . 71 4.2.1 Modulations Schemes for Impulsive Noise Systems . . . 71 4.2.2 Signal Processing Strategies . . . 74 4.2.3 Error Correcting Codes . . . 76 4.3 Synthesis of Impulsive Noise Reduction Techniques . . . . 83 4.4 Proposed Coded-OFDM for Impulsive Noise Mitigation . . . 84 4.4.1 The Proposed Scheme . . . 84 4.4.2 Performance Evaluation . . . 86 4.5 Implementation of the Proposed Approach using GNU Radio

SDR-USRP . . . . 93 4.5.1 Introduction . . . 93 4.5.2 Implementation in GNU Radio . . . 95 4.5.3 Experimental Planning and Testbed Design . . . 97 4.5.4 Performance Analysis . . . 100 4.5.5 Conclusion . . . 109 4.6 Comparison Between Simulations and Measurements . . . . . 110 4.7 Conclusion . . . . 111

70 Chapter 4 : Impulsive noise mitigation with coded-OFDM system

4.1

Introduction

Wireless communication systems continue to draw a symbolic role in the modernization grid. The deployment of WSNs in HV substations is becoming essential for the develop- ment of a smart electrical network. WSNs have several characteristics that make them an interesting solution for a harsh environment such as a power station. Such systems are used to increase communications in the electric power system for improving reliability and efficiency. Sensors are installed in substations for monitoring, diagnostics, and main- tenance of the system. Because of their ease of deployment, cost saving, and flexibility, wireless systems are potential candidates compared to wireline deployments [75]. However, the wireless communication channel is dynamic and unpredictable. The behavior of radio waves is affected by phenomena such as interference or jamming, reflection, and diffraction. Besides, the deployment of WSNs in substations necessitates considering their specificities: metallic structures and devices may produce a specific and robust radio noise which is impulsive.

In HV environments, the ambient or thermal noise commonly considered as Gaussian noise are insufficient; the impulsive noise needs to be taken into account. Because of transient processes, electronic switching, and PD, the noise environment may be highly impulsive [76], [25]. Impulsive noises are characterized by short duration, and their inter- ference levels are vast [67]. The performance and the efficiency of a wireless system can be degraded due to the impulsiveness of the interference. To enable smart grid applica- tions, the design of a robust wireless communication system is growing in interest. Several types of research have been done in this domain. Recent publications show that impulsive noise appearing in electricity substations can degrade the performance of ZigBee systems [77-80]. In Bhatti’s work, an impulsive noise model based on the Symmetric Alpha-Stable distribution was proposed to evaluate the performance of ZigBee. It also compared WLAN technology and ZigBee systems. Simulation results showed that the degradation perfor- mance of WLAN is more severe compared to the Gaussian noise environment whereas, for ZigBee systems, the degradation is modest [77], [78]. Ali presented several results in [79]. He showed that the performance of the communication system is degraded when the receiver is altered by impulsive noise. In our previous chapter, we studied the impact of impulsive noise on a ZigBee receiver for AWGN, Rayleigh, and Rician channels. The results showed that the impulsive noise influence is close to a Gaussian noise or a Rayleigh noise according to the SNR. Also, many smart grid applications require a high data rate and a broad bandwidth. In [81], the authors addressed the feasibility of wireless technologies deployment in power stations. Their analysis underlined that some requirements such as the interoperability and the bandwidth are inadequate. For solving the problems linked to wireless sensor protocols proposed in the literature and to enable smart grid applications, we propose a joint solution by combining forward error correction codes (FEC codes) and OFDM. OFDM is a well-known technique employed in wireless communications. It has several advantages over single carrier systems. OFDM’s main advantage is its resistance to frequency selectivity. Moreover, coded OFDM allows the exploitation of frequency di- versity and provides a total resistance to impulse noise and fast fades. Reed-Solomon (RS)

code, CC scheme, Turbo code, and Low Density Parity Check codes (LDPC) are common FEC codes associated with the coded OFDM systems [82].

This chapter aims to provide a proposal for a physical layer suitable for electricity substa- tions to enable SG operations. The rest of this chapter is organized as follows. Section 2 reviews the impulsive noise reduction techniques, while section 3 concerns its synthesis. The method that we propose for impulsive noise cancellation is presented in section 4. Section 5 is about the implementation of the coded-OFDM system in GNU Radio. The comparison between simulation and measurements is provided in section 6. A conclusion is made in section 7.